મે 29, 2011
Iwan Fals - Isi Rimba Tak Ada Tempat Berpijak Lagi
Isi Rimba Tak Ada Tempat Berpijak Lagi
Raung buldozer gemuruh pohon tumbang
Berpadu dengan jerit isi rimba raya
Tawa kelakar badut-badut serakah
Tanpa HPH berbuat semaunya
Lestarikan alam hanya celoteh belaka
Lestarikan alam mengapa tidak dari dulu
Oh mengapa
Ohohoooo
Jelas kami kecewa
Menatap rimba yang dulu perkasa
Kini tinggal cerita pengantar lelap si buyung
Bencana erosi selalu datang menghantui
Tanah kering kerontang
Banjir datang itu pasti
Isi rimba tak ada tempat berpijak lagi
Punah dengan sendirinya akibat rakus manusia
Lestarikan hutan hanya celoteh belaka
Lestarikan hutan mengapa tidak dari dulu saja
ohohoooo
Jelas kami kecewa
Mendengar gergaji tak pernah berhenti
Demi kantong pribadi
Tak ingat rejeki generasi nanti
Bencana erosi selalu datang menghantui
Tanah kering kerontang
Banjir datang itu pasti
Isi rimba tak ada tempat berpijak lagi
Punah dengan sendirinya akibat rakus manusia
Iwan Fals - Pohon Kehidupan
POHON KEHIDUPAN
Hari baru telah datang menjelang
Kehidupan terus berjalan
Pohon-pohon jadikan teman
Kehidupan agar tak terhenti
Bukalah hati
Rentangkan tanganmu
Bumi luas terbentang
Satukan hati
Tanam tak henti
Pohon untuk kehidupan
Di hatiku ada pohon
Di hatimu ada pohon
Pohon untuk kehidupan
Tentram damai
Hidup rukun saling percaya
Hijau rindang sekitar kita
Andai esok kiamat tiba
Tanam pohon jangan di tunda
Terus tanam jangan berhenti
Alam lestari
Hidup tak bakal berhenti
In an Convinient of Truth: The Planetary Emergency of Global Warming and What We Can Do About It
An Inconvenient Truth—Gore’s groundbreaking, battle cry of a follow-up to the bestselling Earth in the Balance—is being published to tie in with a documentary film of the same name. Both the book and film were inspired by a series of multimedia presentations on global warming that Gore created and delivers to groups around the world. With this book, Gore, who is one of our environmental heroes—and a leading expert—brings together leading-edge research from top scientists around the world; photographs, charts, and other illustrations; and personal anecdotes and observations to document the fast pace and wide scope of global warming. He presents, with alarming clarity and conclusiveness—and with humor, too—that the fact of global warming is not in question and that its consequences for the world we live in will be disastrous if left unchecked. This riveting new book—written in an accessible, entertaining style—will open the eyes of even the most skeptical.
Climate Change
The issue of Climate Change is an environmental issue of the most widely touted for environmental activists, energy-saving campaign is a campaign that is always organized by government, whether it was saving fuel, electricity and other energy. Actually we should not because it saves me-too want to actively participate in reducing the impact of global warming and climate change but should be viewed from the economic aspect of our energy reserves are dwindling day by day.
Are we not allowed to campaign for environmentally friendly living from yourself? Is fine but more important is to encourage industrial countries (United States) so that helped develop a low carbon economy and urged developed countries to economize on energy consumption of our many-fold in Indonesia. This is the name of social justice-the real economy.
Let's look at the data; in the United States, the rate of car ownership is one car for every 2.2 people. Compare this with India that every single car is owned by an average of 145.9 people. Of that figure, can be estimated how much energy consumption inequality between the developed and developing countries, so do not be surprised if per capita emissions of rich nations like the United States and Australia reached 20.14 and 20.24 far exceeded the world average is only 4, 37 tons of carbon per year.
The issue of climate change can not be separated from the political-economic interests. Carbon emissions is a mirror of progress (and to some degree, the prosperity) of a nation. Reducing emissions means to slow the economic development of a nation. So, when Americans shout that China is building without a care about the environment, it actually means the Americans want China remain poor.
The phenomenon now that we see is the euphoria of love that his impression of environmental bandwagon for young people, but have we capture the real meaning of this phenomenon of climate change ....
Excerpted from various sources ..
મે 14, 2011
મે 10, 2011
Scientists Explore Connections Between Climate, Land Use and Dead Zones
April 22, 2011
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AUSTIN, Texas — Researchers at The University of Texas at Austin and colleagues will use a three-year, $1.5 million grant from NASA to develop computer models to study how changes in climate and land use affect watersheds and coastal ecosystems, seeking to improve understanding of the Texas coast, including dead zones that form in the Gulf of Mexico.
"We'll be able to try different 'what if' experiments and find out what happens when you change variables like irrigation, fertilizer use, urbanization and dams, " said Zong-Liang Yang, a professor in the university's Jackson School of Geosciences and the project's principal investigator.
Yang believes the research will help policy makers, farmers and individuals gauge the impact of their actions on coastal ecosystems.
"The goal is sustainable development," Yang said.
One of the greatest threats to coastal estuaries and bays is eutrophication, a process in which excessive nutrients such as nitrogen cause harmful algal blooms that remove oxygen from the water and kill fish and shellfish, creating a dead zone. Research suggests excessive use of fertilizers in agriculture is a major cause of dead zones. Less certain is how climate change and the shifting of water through dams, diversions and withdrawals affect the delivery of nutrients to the coast.
"Most people don't think about how what they do in one place affects other places far away," said Yang. "But a healthy coast is important for tourism, fisheries and the entire state's economy."
In the first phase of the project, scientists will integrate a series of models — dealing with regional and global climate, weather, land surface, river flow, chemistry and ecosystems — into a unified model framework to study the impacts of land use change and climate change.
"We think as the world warms, we'll experience more intense rainstorms," said Yang. "We'll use our model to study how that might affect the formation of dead zones."
Although the models will initially focus on the contiguous U.S. and surrounding oceans, especially the Gulf of Mexico, the researchers are developing them with the flexibility to be applied to other parts of the world that experience widespread and severe dead zones, such as southeast Asia.
The models will run on the supercomputers of the Texas Advanced Computing Center at The University of Texas at Austin, among the fastest in the world.
In addition to Yang, the team includes six co-investigators: David Maidment and James McClelland at The University of Texas at Austin; Paul Montagna and Hae-Cheol Kim at Texas A&M University at Corpus Christi; Hongjie Xie at The University of Texas at San Antonio; and Wei Min Hao at the U.S. Forest Service. The team also includes five collaborators: Nicole Smith-Downey at The University of Texas at Austin; Christine Wiedinmyer at the National Center for Atmospheric Research; Guo-Yue Niu at Biosphere 2 Inc., University of Arizona; Jianhong Xue at Virginia Institute of Marine Sciences; and Gregory E. Schwarz at the U.S. Geological Survey.
For more information, contact: Marc Airhart, Geology Foundation, Jackson School of Geosciences, 512 471 2241.
Tags: Support, algal bloom, climate change, coastal ecosystem, computer model, dead zone, eutrophication, fertilizer runoff, global warming, Gulf of Mexico, hypoxic, Jackson School of Geosciences, NASA, nitrogen, red tide, Research, sustainability, sustainable development, Texas Advanced Computing Center, watershed, zong-liang yang
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2 Comments to "Scientists Explore Connections Between Climate, Land Use and Dead Zones"
1. Sam Nettles said on April 28, 2011
Are harmful algal blooms the only cause of dead zones?
2. Liang Yang said on May 3, 2011
Harmful algal blooms are an important cause of dead zones, but this is not true everywhere. The dead zone in the Black Sea is not caused by pollutants. Also, you may find this site about the Gulf Coast dead zone interesting.
April 22, 2011
Share this story: These icons link to social bookmarking sites where readers can share and discover new web pages.
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AUSTIN, Texas — Researchers at The University of Texas at Austin and colleagues will use a three-year, $1.5 million grant from NASA to develop computer models to study how changes in climate and land use affect watersheds and coastal ecosystems, seeking to improve understanding of the Texas coast, including dead zones that form in the Gulf of Mexico.
"We'll be able to try different 'what if' experiments and find out what happens when you change variables like irrigation, fertilizer use, urbanization and dams, " said Zong-Liang Yang, a professor in the university's Jackson School of Geosciences and the project's principal investigator.
Yang believes the research will help policy makers, farmers and individuals gauge the impact of their actions on coastal ecosystems.
"The goal is sustainable development," Yang said.
One of the greatest threats to coastal estuaries and bays is eutrophication, a process in which excessive nutrients such as nitrogen cause harmful algal blooms that remove oxygen from the water and kill fish and shellfish, creating a dead zone. Research suggests excessive use of fertilizers in agriculture is a major cause of dead zones. Less certain is how climate change and the shifting of water through dams, diversions and withdrawals affect the delivery of nutrients to the coast.
"Most people don't think about how what they do in one place affects other places far away," said Yang. "But a healthy coast is important for tourism, fisheries and the entire state's economy."
In the first phase of the project, scientists will integrate a series of models — dealing with regional and global climate, weather, land surface, river flow, chemistry and ecosystems — into a unified model framework to study the impacts of land use change and climate change.
"We think as the world warms, we'll experience more intense rainstorms," said Yang. "We'll use our model to study how that might affect the formation of dead zones."
Although the models will initially focus on the contiguous U.S. and surrounding oceans, especially the Gulf of Mexico, the researchers are developing them with the flexibility to be applied to other parts of the world that experience widespread and severe dead zones, such as southeast Asia.
The models will run on the supercomputers of the Texas Advanced Computing Center at The University of Texas at Austin, among the fastest in the world.
In addition to Yang, the team includes six co-investigators: David Maidment and James McClelland at The University of Texas at Austin; Paul Montagna and Hae-Cheol Kim at Texas A&M University at Corpus Christi; Hongjie Xie at The University of Texas at San Antonio; and Wei Min Hao at the U.S. Forest Service. The team also includes five collaborators: Nicole Smith-Downey at The University of Texas at Austin; Christine Wiedinmyer at the National Center for Atmospheric Research; Guo-Yue Niu at Biosphere 2 Inc., University of Arizona; Jianhong Xue at Virginia Institute of Marine Sciences; and Gregory E. Schwarz at the U.S. Geological Survey.
For more information, contact: Marc Airhart, Geology Foundation, Jackson School of Geosciences, 512 471 2241.
Tags: Support, algal bloom, climate change, coastal ecosystem, computer model, dead zone, eutrophication, fertilizer runoff, global warming, Gulf of Mexico, hypoxic, Jackson School of Geosciences, NASA, nitrogen, red tide, Research, sustainability, sustainable development, Texas Advanced Computing Center, watershed, zong-liang yang
Receive new post updates: Entries (RSS)
Receive follow up comments updates: RSS 2.0
2 Comments to "Scientists Explore Connections Between Climate, Land Use and Dead Zones"
1. Sam Nettles said on April 28, 2011
Are harmful algal blooms the only cause of dead zones?
2. Liang Yang said on May 3, 2011
Harmful algal blooms are an important cause of dead zones, but this is not true everywhere. The dead zone in the Black Sea is not caused by pollutants. Also, you may find this site about the Gulf Coast dead zone interesting.
Planetary Scientists Solve 40-year-old Mysteries of Mars’ Northern Ice Cap
May 26, 2010
AUSTIN, Texas--Scientists have reconstructed the formation of two curious features in the northern ice cap of Mars—a chasm larger than the Grand Canyon and a series of spiral troughs—solving a pair of mysteries dating back four decades while finding new evidence of climate change on Mars.
In a pair of papers to be published in the journal Nature on May 27, Jack Holt and Isaac Smith of The University of Texas at Austin’s Institute for Geophysics and their colleagues describe how they used radar data collected by NASA’s Mars Reconnaissance Orbiter to reveal the subsurface geology of the red planet’s northern ice cap.
On Earth, large ice sheets are shaped mainly by ice flow. But on Mars, according to this latest research, other forces have shaped, and continue to shape, the polar ice caps.
The northern ice cap is a stack of ice and dust layers up to two miles (three kilometers) deep covering an area slightly larger than Texas. Analyzing radar data on a computer, scientists can peel back the layers like an onion to reveal how the ice cap evolved over time.
Enlarged Image
(top panel) Perspective view of northern polar cap of Mars, looking up Chasma Boreale. Yellow line indicates ground track of SHARAD in orbit. (bottom panel) Cutaway view of same, showing subsurface layers as viewed by SHARAD. Credit: NASA/Caltech/JPL/E. DeJong/J. Craig/M. Stetson
One of the most distinctive features of the northern ice cap is Chasma Boreale, a canyon about as long as the Grand Canyon but deeper and wider. Some scientists have suggested Chasma Boreale was created when volcanic heat melted the bottom of the ice sheet and triggered a catastrophic flood. Others have suggested strong polar winds, called katabatics, carved the canyon out of a dome of ice.
Other enigmatic features are troughs that spiral outward from the center of the ice cap like a gigantic pinwheel. Since they were discovered in 1972, scientists have proposed several hypotheses for how they formed. One suggested that as the planet spins, ice closer to the poles moves slower than ice farther from the poles, causing the semi-fluid ice to crack. Another used an elaborate mathematical model to suggest how increased solar heating in certain areas and lateral heat conduction could cause the troughs to self assemble.
It turns out both the spiral troughs and Chasma Boreale were created and shaped primarily by wind. But rather than being cut into existing ice very recently, the features formed over millions of years as the ice sheet itself grew. By influencing wind patterns, the topography of underlying, older ice controlled where and how the features grew. Topography is the three-dimensional shape of a surface, including peaks, valleys, slopes and plains.
Before this research, conventional wisdom held that the northern ice cap of Mars was made of many relatively flat layers like a layered cake. It was assumed some climate information would be recorded in the layers, limited to what could be gained from layer thickness and dust content. This research, however, reveals many complex features—including layers that change in thickness and orientation, or abruptly disappear in some places—making it a virtual gold mine of climate information.
"Nobody realized that there would be such complex structures in the layers,” says Holt, lead author of the paper focusing on Chasma Boreale. “The layers record a history of ice accumulation, erosion and wind transport. From that, we can recover a history of climate that’s much more detailed than anybody expected.”
Enlarged Image
Two intersecting radar paths slice a wedge in the north polar ice cap of Mars, revealing layers in the ice. These layers can help scientists reconstruct the formation of the ice cap and changes in ancient climate. Colors represent surface elevation of the ice cap. Credit: NASA/JPL/University of Texas at Austin/Prateek Choudhary
The spiral trough results vindicate an early explanation that had fallen out of favor in parts of the Mars scientific community. Alan Howard, a researcher at the University of Virginia, proposed just such a process in 1982 based solely on images of the surface from the Viking mission.
“He only had Viking images with relatively low resolution,” says Isaac Smith, doctoral student and lead author on the spiral trough paper. Holt is second author on the trough paper. “Many people proposed other hypotheses suggesting he was wrong. But when you look at a hypothetical cross section from his paper, it looks almost exactly like what we see in the radar data.”
Why are the troughs spiral shaped? First, katabatic winds are caused by relatively cold, dense air that rolls down from the poles and out over the ice cap. Second, as they blow down, they are deflected by the Coriolis force, which is caused by the planet’s spinning in space. On Earth, this is what causes hurricanes to spin opposite directions in opposite hemispheres. This force twists the winds—and the troughs they create—into spiral shapes.
These breakthroughs were made possible by a new instrument called Shallow Radar (SHARAD). Similar instruments have been used on aircraft in Antarctica and Greenland, but before its use at Mars, some scientists were skeptical it would be able to collect useful data from orbit. Holt is a Co-Investigator on SHARAD.
"These anomalous features have gone unexplained for 40 years because we have not been able to see what lies beneath the surface,” said Roberto Seu, team leader for the SHARAD instrument. “It is gratifying to me that with this new instrument we can finally explain them."
SHARAD is provided to NASA by the Italian Space Agency. It has been designed and developed and is operated by a joint team formed by Sapienza University of Rome’s INFOCOM Department and Thales Alenia Space Italy.
Enlarged Image
Example of SHARAD data from a portion of the north polar cap of Mars showing internal structure of ice. Approximately 2 km thick and 250 km across. Reflectors show changing geometry of subsurface layers related to the deposition and erosion of polar ice through time. Credit: NASA/Caltech/JPL/MRO and SHARAD Team
Co-authors on the paper “The Construction of Chasma Boreale on Mars” include Kathryn Fishbaugh (Smithsonian National Air and Space Museum), Shane Byrne (Lunar and Planetary Laboratory, University of Arizona), Sarah Christian (University of Texas Institute for Geophysics and Bryn Mawr College), Kenneth Tanaka (Astrogeology Science Center, U. S. Geological Survey), Patrick Russell (Planetary Science Institute), Ken Herkenhoff (Astrogeology Science Center, U. S. Geological Survey), Ali Safaeinili (Jet Propulsion Laboratory), Nathaniel Putzig (Southwest Research Institute) and Roger Phillips (Southwest Research Institute).
Funding was provided by NASA and the Gayle White Fellowship at the Institute for Geophysics.
MORE INFO FOR JSG WEBSITE
Enlarged Image
Example of SHARAD data from a portion of the north polar cap of Mars showing internal structure of ice. Approximately 2 km thick and 250 km across. Reflectors show changing geometry of subsurface layers related to the deposition and erosion of polar ice through time. Credit: NASA/Caltech/JPL/MRO and SHARAD Team
The scientists discovered that a combination of wind and pre-existing topography led to the formation of Chasma Borealle and the spiral troughs. Here’s more detail on that underlying topography.
The radar data reveal that before the formation of the current northern ice cap, technically known as the Northern Polar Layered Deposits (NPLD), there was an older and smaller dome of ice and dust at the north pole. Along the edge of that earlier dome, there were sharp steps down to the surrounding plain. At some point, climate changed in way that allowed for an ice cap covering a much larger area. New layers of ice draped over the existing ice cap and out over the surrounding plain. Along the edge of the original dome, strong polar winds called katabatic winds periodically scoured away young ice leaving behind Chasma Boreale. In other words, the pre-existing topography (with help from wind) created a region where new ice couldn’t build up as quickly, even as the new ice cap was growing all around.
The radar data also reveal that the first spiral troughs began to appear sometime in the last 2 million years, and a second generation of troughs came about some time after. The ice sheet was fairly flat, but there were areas where the surface had a slight slope. Troughs began to form near these slopes because of wind speed variations. Katabatic winds picked up ice crystals from the slope, carried them briefly across the terrain, and then deposited them later on, when the wind speed slowed. Over time, this formed dunes that grew while the troughs deepened and migrated as much as 65 kilometers (40 miles) toward the pole. The troughs have continued to grow and migrate up to the present.
The various hypotheses that had been put forward to explain the chasma and the troughs can be lumped into those that involve laying down the ice sheet first and then carving out the features and those that involve somehow preferentially adding ice in some places and not adding it in others. All of the first set of hypotheses predict that when you look at the radar data in the area of a spiral trough, you should be able to trace a layer straight across from one side to the other even though material is missing. It turns out that they don’t see that in the radar data. Instead, they see a layer come in on one side of a trough and then reappear at a different elevation with a different thickness on the other side. This effectively rules out an entire class of hypotheses and constrains the remaining few.
“We’ve ruled out at least half a dozen hypotheses,” says Smith. “Since we know what didn’t happen, we can now focus a lot of people’s energy on what actually happened.”
In addition to wind and topography, these features may have grown with help from the sun. Features on the surface of the ice that face away from the pole receive more sunlight and heat than features facing toward the pole. This causes more ice to be vaporized and stripped away from some areas and allows the wind to carry the water vapor to other areas where it can precipitate onto the surface as ice crystals.
Scientists looking at radar data gathered on Earth from East Antarctica have identified features called Megadunes that are similar to Martian troughs. They aren’t as long or deep as the ones on Mars, aren’t spiral shaped, and they formed over a much shorter time period. The Antarctic troughs aren’t as big because they didn’t have as long to grow. They aren’t spiral shaped because the wind patterns blowing over East Antarctica are much more complicated than the katabatic winds at the Martian north pole. Still, Smith says they may be useful analogs for what happened on Mars.
Curiously, the first spiral troughs began forming after about three-quarters of the ice cap formed.
“This suggests that there was a big change in climate on Mars around that time,” says Smith.
A second set of spiral troughs formed later, suggesting a second change in climate. The researchers also discovered the troughs migrated towards the poles over time. The rate at which they moved provides still more clues about what climate was like at various times. That’s because the speed of migration is directly related to the speeds of the katabatic winds and the rate of ice deposition. Taken together, these observations could allow scientists to make much more accurate models of past climate on Mars.
"Explaining these anomalous features that people have wondered about for 40 years is very exciting,” says Holt. “To me what’s most exciting is discovering that there is a rich history of climate processes and events that are within the polar cap that we can recover using radar."
Over the past 10 years, evidence has mounted that Mars has experienced major episodes of global glaciation in which water ice was transferred between the poles and lower latitudes. Part of that history was revealed last year when Holt and his colleagues announced the discovery of giant glaciers of water ice on Mars at mid-latitudes. Their latest research may provide the key to better understanding past episodes of glaciation.
"We've found a rich and complex record of climate processes stored in polar deposits that we can recover from the radar,” says Holt. “That's important because now we can start to link changes in the polar ice to these glacial periods where ice moved from the poles to the middle latitudes."
For a gallery of images, go to: http://www.jsg.utexas.edu/galleries/mars_north_pole052610/
May 26, 2010
AUSTIN, Texas--Scientists have reconstructed the formation of two curious features in the northern ice cap of Mars—a chasm larger than the Grand Canyon and a series of spiral troughs—solving a pair of mysteries dating back four decades while finding new evidence of climate change on Mars.
In a pair of papers to be published in the journal Nature on May 27, Jack Holt and Isaac Smith of The University of Texas at Austin’s Institute for Geophysics and their colleagues describe how they used radar data collected by NASA’s Mars Reconnaissance Orbiter to reveal the subsurface geology of the red planet’s northern ice cap.
On Earth, large ice sheets are shaped mainly by ice flow. But on Mars, according to this latest research, other forces have shaped, and continue to shape, the polar ice caps.
The northern ice cap is a stack of ice and dust layers up to two miles (three kilometers) deep covering an area slightly larger than Texas. Analyzing radar data on a computer, scientists can peel back the layers like an onion to reveal how the ice cap evolved over time.
Enlarged Image
(top panel) Perspective view of northern polar cap of Mars, looking up Chasma Boreale. Yellow line indicates ground track of SHARAD in orbit. (bottom panel) Cutaway view of same, showing subsurface layers as viewed by SHARAD. Credit: NASA/Caltech/JPL/E. DeJong/J. Craig/M. Stetson
One of the most distinctive features of the northern ice cap is Chasma Boreale, a canyon about as long as the Grand Canyon but deeper and wider. Some scientists have suggested Chasma Boreale was created when volcanic heat melted the bottom of the ice sheet and triggered a catastrophic flood. Others have suggested strong polar winds, called katabatics, carved the canyon out of a dome of ice.
Other enigmatic features are troughs that spiral outward from the center of the ice cap like a gigantic pinwheel. Since they were discovered in 1972, scientists have proposed several hypotheses for how they formed. One suggested that as the planet spins, ice closer to the poles moves slower than ice farther from the poles, causing the semi-fluid ice to crack. Another used an elaborate mathematical model to suggest how increased solar heating in certain areas and lateral heat conduction could cause the troughs to self assemble.
It turns out both the spiral troughs and Chasma Boreale were created and shaped primarily by wind. But rather than being cut into existing ice very recently, the features formed over millions of years as the ice sheet itself grew. By influencing wind patterns, the topography of underlying, older ice controlled where and how the features grew. Topography is the three-dimensional shape of a surface, including peaks, valleys, slopes and plains.
Before this research, conventional wisdom held that the northern ice cap of Mars was made of many relatively flat layers like a layered cake. It was assumed some climate information would be recorded in the layers, limited to what could be gained from layer thickness and dust content. This research, however, reveals many complex features—including layers that change in thickness and orientation, or abruptly disappear in some places—making it a virtual gold mine of climate information.
"Nobody realized that there would be such complex structures in the layers,” says Holt, lead author of the paper focusing on Chasma Boreale. “The layers record a history of ice accumulation, erosion and wind transport. From that, we can recover a history of climate that’s much more detailed than anybody expected.”
Enlarged Image
Two intersecting radar paths slice a wedge in the north polar ice cap of Mars, revealing layers in the ice. These layers can help scientists reconstruct the formation of the ice cap and changes in ancient climate. Colors represent surface elevation of the ice cap. Credit: NASA/JPL/University of Texas at Austin/Prateek Choudhary
The spiral trough results vindicate an early explanation that had fallen out of favor in parts of the Mars scientific community. Alan Howard, a researcher at the University of Virginia, proposed just such a process in 1982 based solely on images of the surface from the Viking mission.
“He only had Viking images with relatively low resolution,” says Isaac Smith, doctoral student and lead author on the spiral trough paper. Holt is second author on the trough paper. “Many people proposed other hypotheses suggesting he was wrong. But when you look at a hypothetical cross section from his paper, it looks almost exactly like what we see in the radar data.”
Why are the troughs spiral shaped? First, katabatic winds are caused by relatively cold, dense air that rolls down from the poles and out over the ice cap. Second, as they blow down, they are deflected by the Coriolis force, which is caused by the planet’s spinning in space. On Earth, this is what causes hurricanes to spin opposite directions in opposite hemispheres. This force twists the winds—and the troughs they create—into spiral shapes.
These breakthroughs were made possible by a new instrument called Shallow Radar (SHARAD). Similar instruments have been used on aircraft in Antarctica and Greenland, but before its use at Mars, some scientists were skeptical it would be able to collect useful data from orbit. Holt is a Co-Investigator on SHARAD.
"These anomalous features have gone unexplained for 40 years because we have not been able to see what lies beneath the surface,” said Roberto Seu, team leader for the SHARAD instrument. “It is gratifying to me that with this new instrument we can finally explain them."
SHARAD is provided to NASA by the Italian Space Agency. It has been designed and developed and is operated by a joint team formed by Sapienza University of Rome’s INFOCOM Department and Thales Alenia Space Italy.
Enlarged Image
Example of SHARAD data from a portion of the north polar cap of Mars showing internal structure of ice. Approximately 2 km thick and 250 km across. Reflectors show changing geometry of subsurface layers related to the deposition and erosion of polar ice through time. Credit: NASA/Caltech/JPL/MRO and SHARAD Team
Co-authors on the paper “The Construction of Chasma Boreale on Mars” include Kathryn Fishbaugh (Smithsonian National Air and Space Museum), Shane Byrne (Lunar and Planetary Laboratory, University of Arizona), Sarah Christian (University of Texas Institute for Geophysics and Bryn Mawr College), Kenneth Tanaka (Astrogeology Science Center, U. S. Geological Survey), Patrick Russell (Planetary Science Institute), Ken Herkenhoff (Astrogeology Science Center, U. S. Geological Survey), Ali Safaeinili (Jet Propulsion Laboratory), Nathaniel Putzig (Southwest Research Institute) and Roger Phillips (Southwest Research Institute).
Funding was provided by NASA and the Gayle White Fellowship at the Institute for Geophysics.
MORE INFO FOR JSG WEBSITE
Enlarged Image
Example of SHARAD data from a portion of the north polar cap of Mars showing internal structure of ice. Approximately 2 km thick and 250 km across. Reflectors show changing geometry of subsurface layers related to the deposition and erosion of polar ice through time. Credit: NASA/Caltech/JPL/MRO and SHARAD Team
The scientists discovered that a combination of wind and pre-existing topography led to the formation of Chasma Borealle and the spiral troughs. Here’s more detail on that underlying topography.
The radar data reveal that before the formation of the current northern ice cap, technically known as the Northern Polar Layered Deposits (NPLD), there was an older and smaller dome of ice and dust at the north pole. Along the edge of that earlier dome, there were sharp steps down to the surrounding plain. At some point, climate changed in way that allowed for an ice cap covering a much larger area. New layers of ice draped over the existing ice cap and out over the surrounding plain. Along the edge of the original dome, strong polar winds called katabatic winds periodically scoured away young ice leaving behind Chasma Boreale. In other words, the pre-existing topography (with help from wind) created a region where new ice couldn’t build up as quickly, even as the new ice cap was growing all around.
The radar data also reveal that the first spiral troughs began to appear sometime in the last 2 million years, and a second generation of troughs came about some time after. The ice sheet was fairly flat, but there were areas where the surface had a slight slope. Troughs began to form near these slopes because of wind speed variations. Katabatic winds picked up ice crystals from the slope, carried them briefly across the terrain, and then deposited them later on, when the wind speed slowed. Over time, this formed dunes that grew while the troughs deepened and migrated as much as 65 kilometers (40 miles) toward the pole. The troughs have continued to grow and migrate up to the present.
The various hypotheses that had been put forward to explain the chasma and the troughs can be lumped into those that involve laying down the ice sheet first and then carving out the features and those that involve somehow preferentially adding ice in some places and not adding it in others. All of the first set of hypotheses predict that when you look at the radar data in the area of a spiral trough, you should be able to trace a layer straight across from one side to the other even though material is missing. It turns out that they don’t see that in the radar data. Instead, they see a layer come in on one side of a trough and then reappear at a different elevation with a different thickness on the other side. This effectively rules out an entire class of hypotheses and constrains the remaining few.
“We’ve ruled out at least half a dozen hypotheses,” says Smith. “Since we know what didn’t happen, we can now focus a lot of people’s energy on what actually happened.”
In addition to wind and topography, these features may have grown with help from the sun. Features on the surface of the ice that face away from the pole receive more sunlight and heat than features facing toward the pole. This causes more ice to be vaporized and stripped away from some areas and allows the wind to carry the water vapor to other areas where it can precipitate onto the surface as ice crystals.
Scientists looking at radar data gathered on Earth from East Antarctica have identified features called Megadunes that are similar to Martian troughs. They aren’t as long or deep as the ones on Mars, aren’t spiral shaped, and they formed over a much shorter time period. The Antarctic troughs aren’t as big because they didn’t have as long to grow. They aren’t spiral shaped because the wind patterns blowing over East Antarctica are much more complicated than the katabatic winds at the Martian north pole. Still, Smith says they may be useful analogs for what happened on Mars.
Curiously, the first spiral troughs began forming after about three-quarters of the ice cap formed.
“This suggests that there was a big change in climate on Mars around that time,” says Smith.
A second set of spiral troughs formed later, suggesting a second change in climate. The researchers also discovered the troughs migrated towards the poles over time. The rate at which they moved provides still more clues about what climate was like at various times. That’s because the speed of migration is directly related to the speeds of the katabatic winds and the rate of ice deposition. Taken together, these observations could allow scientists to make much more accurate models of past climate on Mars.
"Explaining these anomalous features that people have wondered about for 40 years is very exciting,” says Holt. “To me what’s most exciting is discovering that there is a rich history of climate processes and events that are within the polar cap that we can recover using radar."
Over the past 10 years, evidence has mounted that Mars has experienced major episodes of global glaciation in which water ice was transferred between the poles and lower latitudes. Part of that history was revealed last year when Holt and his colleagues announced the discovery of giant glaciers of water ice on Mars at mid-latitudes. Their latest research may provide the key to better understanding past episodes of glaciation.
"We've found a rich and complex record of climate processes stored in polar deposits that we can recover from the radar,” says Holt. “That's important because now we can start to link changes in the polar ice to these glacial periods where ice moved from the poles to the middle latitudes."
For a gallery of images, go to: http://www.jsg.utexas.edu/galleries/mars_north_pole052610/
મે 04, 2011
A Systemic Approach to Managing Natural Disasters
1
Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibite
A Systemic Approach to
Managing Natural Disasters
Jaime Santos-Reyes
SEPI-ESIME, IPN, Mexico
Alan N. Beard
Heriot-Watt University, Scotland
inTroduCTion
Natural disasters may be defined as events that
are triggered by natural phenomena or natural
hazards (e.g., earthquakes, hurricanes, floods,
windstorms, landslides, volcanic eruptions and
wildfires). Throughout history, natural disasters
have exerted a heavy toll of death and suffering
and are increasing alarmingly worldwide. During
the past two decades they have killed millions of
people, and adversely affected the life of at least
one billion people. For example, recent disasters,
such as the quake that triggered a tsunami in the
Indian Ocean (United Nations Development Programme
[UNDP], 2005); earthquake in Pakistan
(Kamp et al., 2008); the Wenchuan earthquake in
China (Zhao et al., 2009) and more recently the
L’Aquila earthquake in Italy (Owen & Bannerman,
2009). On the other hand, hurricanes have
shown how vulnerable coastal communities could
be to such events. For instance, Hurricane Katrina
caused an estimated $35 to $60 billion in damage
aBsTraCT
The objective of this chapter is to present a Systemic Disaster Management System (SDMS) model. The
SDMS model is intended to provide a sufficient structure for effective disaster management. It may be
argued that it has a fundamentally preventive potentiality in that if all the subsystems (i.e., systems 1-5)
and channels of communication are present and working effectively, the probability of failure should
be less than otherwise. Moreover, the model is capable of being applied proactively in the case of the
design of a new ‘disaster management system’ as well as reactively. In the latter case, a past disaster
may be examined using the model as a ‘template’ for comparison. In this way, lessons may be learned
from past disasters. It may also be employed as a ‘template’ to examine an existing ‘disaster management
system’. It is hoped that this approach will lead to more effective management of natural disasters.
DOI: 10.4018/978-1-61520-987-3.ch001
2
A Systemic Approach to Managing Natural Disasters
and resulted in at least 1000 deaths in the United
States alone. More recently, on November 2007,
the State of Tabasco, Mexico, has been flooded
and it has been regarded as one of the worst in
more than 50 years. It is believed that the disaster
left more than one million people homeless. Finally,
it is thought that 2008 has been one of the
most devastating years on record; i.e., more than
220,000 people have been killed in 2008 alone.
The above stresses the importance of prevention,
mitigation and preparedness including evacuation
planning in order to mitigate the impact of
natural disasters. Disaster prevention includes
all those activities intended to avoid the adverse
impact of natural hazards (e.g., a decision not to
build houses in a disaster-prone area). Mitigation,
on the other hand, refers to measures that
should be taken in advance of a disaster order to
decrease its impact on society (e.g., developing
building codes). Finally, disaster preparedness
includes pre- and post- emergency measures that
are intended to minimize the loss of life, and to
organize and facilitate timely effective rescue,
relief, and rehabilitation in case of disaster (e.g.,
organizing simulation activities to prepare for an
eventual disaster relief operation).
Given the above, natural disasters present
a great challenge to society today concerning
how they are to be mitigated so as to produce an
acceptable risk is a question which has come to
the fore in dramatic ways in recent years. As a
society we have tended to shift from one crisis to
another and from one bout of crisis management
to another. There is a need to see things in their
entirety, as far as we are able. In relation to disaster
management, it becomes vital to see disaster risk
as a product of a system; to have a ‘systemic’ approach.
Despite this, very little emphasis has been
given by academe, international organizations,
NGO (Non Governmental Organizations), and
practitioners as to what constitutes and defines
an effective disaster management system, both in
terms of structure and process, from a systemic
point of view. This chapter presents a Systemic
Disaster Management System (SDMS) model. The
model is intended to help to maintain disaster risk
within an acceptable range whatever that might
mean. The model is intended to provide a structure
for an effective disaster management system. It
may be argued that it has a fundamentally preventive
potentiality in that if all the sub-systems
and channels of communication are present and
working effectively, the probability of a failure
should be less than otherwise. It is hoped that this
approach will lead to more effective management
of natural disasters
BaCkground
A great deal of effort has been made, by academe,
international organizations, and governments,
practitioners, to investigate and develop approaches
to address disaster risk. For instance,
during the 1990s the United Nations (UN) sponsored
the International Decade for Natural Disaster
Reduction (IDNDR) with the aim of reducing
losses caused by natural hazards (Annan, 1988).
The IDNDR Scientific and Technical Committee
identified five challenges to guide future programs:
(1) Integrate natural disaster management with
overall planning; (2) anticipate mega disasters
due to population concentrations; (3) reduce environmental
and resource vulnerability; (4) improve
disaster prevention capabilities of developing
countries; and (5) assure effective coordination
and implementation. The UN has also established
the International Strategy for Disaster Reduction
(ISDR) which serves as an international information
clearinghouse on disaster reduction, developing
awareness campaigns and producing articles,
journals, and other publications and promotional
materials related to disaster reduction; the publication
of “Living with risk: A global review of
disaster reduction initiatives” document (ISDR,
2004) is an example of these.
Other world organizations and countries have
published a vast amount of reports and publica3
A Systemic Approach to Managing Natural Disasters
tions on the management of disasters; inter alia,
(Colombo & Vetere Arellano, 2002; ECLAC,
1991; Freeman et al., 2002; Jayawardane, 2006;
Kazusa, 2006; Kreimer & Arnold, 2000). Other
authors, such as Vakis (2006) discusses natural
disasters within the general framework of ‘social
risk management’ and highlights the complementary
role that “social protection” can play in the
formation and response of an effective strategy for
natural disasters management system. The author
proposes a number of “social protection” issues
that can be used in practice to address natural
disasters. On the other hand, it is now recognised
that ‘development’ and disasters have a close and
complex relationship. For instance, Mileti et al.
(1995) argue that “losses from natural disasters
occur because of development that is unsustainable”.
Similarly, Stenchion (1997) emphasises
that “development and disaster management are
both aimed at vulnerability reduction”. Some
authors, such as Cuny (1994) argues that development
is often set back by disasters and others
assert that post-disaster operations should take
into account a development perspective (see also
Berke et al., 1993; McAllister, 1993). The United
Nations Development Programme published the
document “Reducing disaster risk: A challenge
for development” (UNDP, 2004). The report in
a way summarizes the above points; i.e., natural
disaster risk is connected to the process of human
development and that disasters put development
at risk. Furthermore, it emphasizes that human
development can also contribute to reduction in
disaster risk. Finally, the report argues that disaster
risk is not inevitable and offers examples of good
practice in disaster risk reduction that can be built
into ongoing development planning policy.
Other researches have concentrated on several
issues regarding disaster management; i.e., organizational,
technological, early warning systems,
economic, emergency, etc. For instance, Granot
(1997) reviews the diverse cultures of different
organizations and a number of findings regarding
emergency services and suggests directions that
may improve inter-organizational relationships.
Kouzmin et al. (1995), on the other hand, discusses
the efficiency of disaster management policies and
programmes in Australia. The authors argue that
there are longstanding deficiencies in strategic and
operational planning and forecasting approaches;
they argue the need for more co-operation and
co-ordination between the various emergency
services, and finally, the authors discuss the development
of terrestrial and space technologies
which could be used in disaster management.
Other authors have concentrated their research
on emergency response preparedness issues. For
example, Wilson (2000) examines small group
training for those in charged with responding in
an emergency situation. Wilson argues that to
ensure both effective and efficient training it is
important to understand that people learn in different
ways. Cosgrave (1996) proposes that decision
making is part of all management tasks and that it
is particularly important for emergency managers
as they often need to take decisions quickly. The
author reviews some of the particular problems
of emergency decision and looks at the usefulness
of Vroom and Yetton’s decision process model for
emergencies (Vroom & Yetton, 1973), before proposing
a simplified problem classification based on
three problem characteristics. Cosgrave concludes
by reviewing a collection of “emergency” decisions
and analysing some of the common factors
to suggest a number of simple action rules to be
used in conjunction with the proposed simplified
decision process model.
Fisher (1998) has investigated the role of
the new information technologies in emergency
mitigation, planning, response and recovery.
The author illustrates the utility of multimedia,
CD-ROM, e-mail and Internet applications to
enhance emergency preparedness. Technologies
such as ‘remote sensing’, GIS (Global Positioning
System) and GPS (Geographical Information
System), also known as ‘3S’ technology, have been
used in the process of monitoring disasters. Murai
(2006) has developed a system for monitoring
4
A Systemic Approach to Managing Natural Disasters
disasters using ‘remote sensing’, GIS and GPS.
The author argues that the developed monitoring
system records the real status of damages due to
natural disasters and analyzes the “cause” of a
disaster and predicts its occurrence. Following the
tsunami disaster in 2004, the General Secretary
of the United Nations (ONU) Kofi Annan called
for a global early warning system for all hazards
and for all communities. He also requested the
ISDR and its UN partners to conduct a global
survey of capacities, gaps and opportunities in
relation to early warning systems (Annan, 2005).
The produced report, “Global Survey of Early
Warning Systems”, concluded that there are many
gaps and shortcomings and that much progress
has been made on early warning systems and
great capabilities are available around the world
(Egeland, 2006). However, it is argued here that it
may be not enough to have such systems without
concentrating on ‘wider’ issues, such a system
where an EWS may be just part of it.
More recently, there has been considerable
interest on the concepts of vulnerability and resilience.
However, there are multiple definitions
of these two concepts in the literature and there
is not an accepted definition (Klein et al., 2003;
Manyena, 2006). For instance, Cutter et al. (2008)
defines vulnerability as the “pre-event, inherent
characteristics or qualities of social systems that
create the potential for harm”. On the other hand,
numerous frameworks, conceptual models, and
vulnerability assessment techniques have been
developed in order to address the theoretical
underpinnings and practical applications of vulnerability
and resilience (Adger, 2006; Burton
et al., 2002; Eakin & Luers, 2006; Fussel, 2007;
Gallopin, 2006; Green & Penning-Rowsell, 2007;
Klein et al., 2003; McLaughlin & Dietz, 2008;
Polsky et al., 2007).
a sdMs Model
The Systemic Disaster Management System
(SDMS) model is intended to maintain disaster
risk within an acceptable range in an organization’s
operations in relation to disaster management.
It may be argued that if all the sub-systems
and channels of communication and control are
present and working effectively, the probability
of a failure should be less than otherwise; in this
sense the model has a fundamentally preventive
potentiality. Table 1 summarizes the main char-
Table 1. Fundamental characteristics of the SDMS model
1 A recursive structure (i.e., ‘layered’) and relative autonomy (RA)
2 A structural organization which consists of a ‘basic unit’ in which it is necessary to achieve five functions associated with systems 1
to 5. (See Figure1).
(a) system 1: disaster-policy implementation
(b) system 2: disaster- national early warning coordination centre (NEWCC)
(c) system 2*: disaster-local early warning coordination centre (LEWCC)
(d) system 3: disaster-functional
(e) system 3*: disaster-audit
(f) system 4: disaster-development
(g) system 4*: disaster-confidential reporting system
(h) system 5: disaster-policy
Note: whenever a line appears in Figure 1 representing the SDMS model, it represents a channel of communication.
3 The SDMS & its ‘environment’
4 The concept of MRA (Maximum Risk Acceptable), Viability and acceptable range of risk.
5 Four principles of organization
6 ‘Paradigms’ which are intended to act as ‘templates’ giving essential features for effective communication and control.
5
A Systemic Approach to Managing Natural Disasters
acteristics of the model and Figure 1 shows the
structural organization of the SDMS model.
recursive structure of
the sdMs Model
A Recursion may be regarded as a ‘level’, which
has other levels below or above it. The concept
of recursion is intended to help to identify the
level of the organization being modelled or being
considered for analysis. Figure 2 is intended to
show three levels of recursion for an organization.
System 1 at level 1 contains the sub-system of
interest; i.e., the ‘National Disaster Operations’
(NDO) which may be taken to be the highest level
of the system of interest (e.g., level of a country).
The sub-system is represented as an elliptical
symbol that contains two essential elements:
1. The ‘National Disaster Management Unit’
(NDMU) represented by a parallelogram
symbol which is concerned with the ‘disaster
risk management’ in the ‘National Disaster
Operations’ (NDO) of the organization, and
2. The NDO, which is where the disaster risks
are created, within system 1, due to the interaction
of all the processes that take place
within a country, region or community. There
may be other risks due to interaction with
the ‘environment’ (see section ‘the SDMS
& its environment’ for further details about
these). Note that the double arrow line connecting
(1) & (2) represent the managerial
interdependence.
Increasing the level of resolution of the system
of interest, i.e., NDO at one level below recursion
Figure 1. A SDMS model
6
A Systemic Approach to Managing Natural Disasters
1 will result in the ‘Zone A-Disaster Operations’
(ZADO) & ‘Zone B-Disaster Operations’ (ZBDO)
and this is shown at level 2 in Figure 2. It must be
pointed out that each of these sub-systems can be
de-composed into further sub-systems depending
on our level of interest. For example, ‘Region-1
Disaster Operations’ (R1DO), ‘Region-2 Disaster
Operations’ (R2DO) and ‘Region-3 Disaster
Operations’ (R3DO) are shown as sub-systems
of the ‘Zone A Disaster Operations’ (ZADO) at
level 3. In principle, each sub-system that forms
part of system 1 at level 3 can be de-composed
further depending on the level of interest of the
‘disaster management system’ modeller or analyst.
relative autonomy (ra)
The SDMS is intended to be able to maintain
disaster risk within an acceptable range at each
level of recursion, but this safety achievement,
at each level, is conditional on the cohesiveness
of the whole organization. The SDMS contains
a structure that favours relative autonomy and
local safety problem-solving capacity. Relative
autonomy means that each operation of system
1 of the SDMS is responsible for its own activity
with minimal intervention of systems 2-5. The
organizational structure of the SDMS allows
decisions to be made at the local level. Decision
making is distributed throughout the whole or-
Figure 2. Recursive structure of the SDMS
7
A Systemic Approach to Managing Natural Disasters
ganization. This means that distributed decision
making involves a set of decision makers in each
operation of system 1 and at each level of recursion.
These decision makers should be relatively
autonomous in their own right and act relatively
independently based on their own understanding of
safety and their specific tasks. However, it should
be recognised that they have interdependence
with other decision makers of other operations
of system 1 (see Figures 3 & 4). Therefore, each
operation of system 1 should be endowed with
relative autonomy so that the organizational safety
policy can be achieved more effectively. Relative
autonomy must not be confused with isolation;
it must be within an adequate system of control
and communication.
sTruCTural organizaTion
of The sdMs Model
The structural organization of the SDMS model
consists of a ‘basic unit’ in which it is necessary
to achieve five functions associated with systems
1 to 5. Systems 2 to 5 facilitate the function of
system 1, as well as ensuring the continuous adaptation
of the disaster management system as a
whole. The operations identified at recursion 2 (see
Figure 2) have been represented in the format of
Figure 3. Disaster management system-in-focus at recursions 1&2
8
A Systemic Approach to Managing Natural Disasters
the structural organization of the model. Figure 3
shows what is called here ‘disaster management
system-in-focus’ at recursions 1&2; similarly,
Figure 4 illustrates the ‘disaster management
system-in-focus’ at recursions 2&3. It should be
emphasized that both Figures should be seen in
the context of Figure 2. Referring to Figures 1&3:
system 1: disaster- Policy
implementation
System 1 may be regarded as the core of the SDMS
model. That is, it is where all the daily activities
within an organization (i.e., country, region, community,
etc.) take place and therefore, it is where
disaster risks are created. How system 1 might
be broken down is a key question; for example,
it might be de-composed on a basis of geography
or functions. For the purpose of the present case
system 1 has been de-composed on a basis of
geography as shown in Figures 2, 3&4.
As illustrated in Figure 1, system 1 is interrelated
with systems 2, 3&3*; i.e., system 1 consists
of several subsystems or operations, such
as ZADO, ZBDO, etc. Table 2 presents some
examples of the information that flows through
these channels of communication.
system 2: disaster- national
early Warning Coordination
Centre (neWCC)
The function of system 2 is to co-ordinate the
activities of the operations of system 1. System
Figure 4. Disaster management system-in-focus at recursions 2&3
9
A Systemic Approach to Managing Natural Disasters
Table 2. Examples of the sort of information that flows through the channels of communication
Communication channel
(see Figure 1) Description/Examples
System
1
System 1 to System 2
channel
Information about the maintenance programmes of physical infrastructure, such as early warning
systems; training programme of evacuation of the population, etc.
System 1 to System 3
channel
Information about: the lack of maintenance of the physical infrastructure; compliance and enforcement
of the legal and regulatory requirements; lack of forecasting systems; the need of new methodologies
for disaster risk identification, analysis and evaluation; the need to improve technologies, for
example, to control flood, etc.
System 1 to System 3*
channel
Compliance of public and private buildings with codes and standards as well as with land use plans;
whether the planned performance associated with the population’s response to an emergency (e.g.,
the effective response of the people, fire-fighters and police in an exercise based on the scenario of
an earthquake occurring) is being achieved or not.
System
2
System 2 to System 1
channel
A wide range of stakeholders need to be coordinated in the operations of system 1; for instance at
government level this means ensuring cross-departmental co-ordination; across society as a whole it
requires better links between the NGOs, the private sector and academia, etc. Coordination amongst
the main actors involved in the early warning chain to provide optimum conditions for informed
decision-making and response actions.
System 2 to System 3
channel
Malfunctioning or failure of a local early warning system (EWS); deficiencies on the channels of
communication between forecast and the intended recipient; i.e., the people from the communities, etc.
System
2*
System 2* to System 1
channel
Monitoring of data related to any particular sensor system; e.g., ocean bottom pressure sensors buoys,
tide gauging, etc. The communications may be achieved via wire line, wireless, satellite, etc.
System 2* to System 2
channel
If a deviation from an accepted criterion occurs then this is reported quickly to system 2.
System
3
System 3 to System 1
channel
Resource allocation for disaster reduction; i.e., financial, human, technical, material; legal and regulatory
requirements; i.e., laws, acts, regulations, codes, standards. For example, national disaster risk
reduction policies; standards (e.g., public and private building codes and standards); education and
training programmes: e.g., inclusion of disaster reduction at all levels of education (curricula, education
material), national and local training programmes; public awareness programmes, etc.
System 3 to System 2
channel
The performance of early warning systems,; the population’s awareness on how to react in case of
an earthquake, hurricane, etc.
System 3 to System 3*
channel
The population’s safety culture, etc.; the adequacy of the design and construction of public and private
houses; the adequacy of the training of evacuation programmes, etc.
System 3 to System 4
channel
System 3 communicates its needs to system 4; i.e., information about new developments on risk assessment
analysis techniques, new technologies, reassessment of process changes, new development
of means of escape, etc.
System
3*
System 3* to System 1
channel
Inadequacy of the design and construction of physical infrastructure; inadequacy of the critical infrastructure;
lack of maintenance of the physical infrastructure; deficiencies in the land use planning, etc.
System 3* to System 3
channel
Deficiencies in the design and construction of public and private houses; deficiencies of the population
on how to react in case of a natural hazard; i.e., an earthquake, etc.; lack of training of evacuation
programmes, etc.
System
4
System 4 to System 3
channel
Research programmes aiming to risk reduction; new methods in disaster risk identification and assessment;
new technologies aiming to improve the physical and technical measures, for example,
flood control techniques, soil conservation practices, retrofitting of building, etc.; modern methods
of monitoring, e.g., crop production, etc.
System 4 to System 5
channel
System 4 could, for example, communicate to system 5 about: the new technologies and regulations
related to the design of buildings identified in the ‘environment’; the development of new technologies
related to the prediction of earthquakes; new techniques in order to improve the flood control, etc.;
the development of new tools for risk assessments that reflect the dynamic nature of danger, such as,
climate change, urban growth, disease, etc.
10
A Systemic Approach to Managing Natural Disasters
2, along with system 1 management units, implements
the safety plans received from system 3.
It informs system 3 about routine information on
the performance of the operations of system 1. To
achieve the plans of system 3 and the needs of
system 1, system 2 gathers and manages the safety
information of system 1’s operations. Moreover,
it also coordinates other local early warning coordination
centres (LEWCCs).
As illustrated in Figure 1, system 2 is interrelated
with systems 1&3. Table 2 presents some
examples of the information that flows through
these channels of communication.
system 2*: disaster- local
early Warning Coordination
Centre (leWCC)
System 2* is part of system 2 and it is responsible
for communicating advance warnings to other
early warning coordination centres and to key
decision makers. This action is intended to help
to take appropriate actions prior to the occurrence
of a major natural hazard event. Santos-Reyes
(2007) gives some details about how this might
be achieved. Table 2 presents some examples of
the information that flows through these channels
of communication.
system 3: disaster- functional
(Monitoring, assessment)
System 3 is directly responsible for maintaining
risk within an acceptable range in system 1, and
ensures that system 1 implements the organization’s
safety policy. It achieves its function on a
day-to-day basis according to its own safety plans
and the strategic and normative safety plans received
from system 4. The purpose of these plans
is to anticipate and act proactively to maintain the
disaster risk, arising from the operations of the
sub-systems that form part of system 1.
As illustrated in Figure 1, system 3 is interrelated
with systems 1, 2, 3*& 4. Table 2 presents
some examples of the information that flows
through these channels of communication.
system 3*: disaster- audit
System 3* is part of system 3 and its function is to
conduct audits sporadically into the operations of
system 1. System 3* intervenes in the operations
of system 1 according to the safety plans received
from system 3. System 3 needs to ensure that the
accountability reports received from system 1
reflect not only the current status of the operations
of system 1, but are also aligned with the overall
objectives of the organization. The audit activities
should be sporadic (i.e., unannounced) and they
should be implemented under common agreement
between system 3* and system 1.
As illustrated in Figure 1, system 3* is interrelated
with systems 1&3. Table 2 presents some
examples of the information that flows through
these channels of communication.
system 4: disaster- development
System 4 is concerned with safety research and
development (R&D) for the continual adaptation
of the disaster management system as a whole.
By considering strengths, weaknesses, threats and
opportunities, system 4 can suggest changes to the
organization’s safety policies. This function may
be regarded as a part of effective safety planning.
System 4 achieves its function according to the
safety policy of system 5; i.e., to maintain disaster
risk within an acceptable range in the organizations
operations. System 4 should sense, scan and
attempt to respond appropriately to the various
threats and opportunities identified in the system’s
‘total environment’ (see Section ‘the SDMS &
Its environment’ for details of the environmental
factors). There are two main safety issues which
11
A Systemic Approach to Managing Natural Disasters
system 4 has to deal with regarding the ‘total
environment’. First, the large broken line elliptic
symbol represents the ‘total environment’ of the
system (see Figures 1, 3&4). Second, system 4
should deal with the ‘disaster future environment’.
The ‘disaster future environment’ is concerned
with threats and opportunities relating to future
development of safety that may be relevant for the
organization. Therefore, the SDMS deals not only
with current safety problems, but also anticipates
or prevents future disasters.
As illustrated in Figure 1, system 4 interacts
with the ‘total environment’, systems 5 &3. Table
2 presents some examples of the information that
flows through these channels of communication.
system 4*: disaster- Confidential
reporting system
System 4* is part of system 4 and is concerned
with confidential reports or causes of concern from
any employee, about any aspects, some of which
may require the direct and immediate intervention
of system 5. This means that system 4* analyses
all information coming through this channel and
develops and plans actions to act upon what has
been reported so that these or similar incidents
or causes of concern do not occur in the future.
system 5: disaster- Policy
System 5 is responsible for deliberating safety
policies and for making normative decisions. According
to alternative safety plans received from
system 4, system 5 considers and chooses feasible
alternatives, which aim to maintain disaster risk
within an acceptable range throughout the life
cycle of the total system. Furthermore, these safety
policies should: reflect the safety values and beliefs
of the whole organization; address the anticipation
of disasters due to natural hazard; promote safety
culture throughout the organization. System 5 also
monitors the interaction of system 3 and system
4, as represented by the lines that show the loop
between systems 3 and 4 as shown in Figures
1&3. The safety policies that are deliberated and
decided by system 5 for implementation should
address, for example, the following issues:
• It should also promote safety culture
throughout the organization.
• Establishment of policy in development
planning: e.g., poverty reduction or eradication,
social protection, sustainable development,
climate change ‘adaptation’,
natural resource management, health, education,
etc.
• Promotion of disaster risk awareness
through education at all levels of the
organization.
Hot-Line
Figures 1, 3 & 4 show a dashed line directly from
system 1 to system 5; it represents a direct channel
of communication or ‘hot-line’ for use in exceptional
circumstances; e.g., during an emergency.
It represents ‘initially’ one-way communication
channel but they may become two way communication
channels between systems 1 and 5.
The sdMs & its environment
‘Environment’ may be understood as those circumstances
to which the SDMS response is necessary.
‘Environment’ lies outside the SDMS but interacts
with it (see Figures 1, 3&4). It influences and is
influenced by the system. Thus, it is important to
consider it. For instance, natural hazards such as
earthquakes, hurricanes, etc, threaten the system;
so that these hazards and the associated risks should
be eliminated or controlled. In addition, table 3
lists some ‘environmental’ factors that should be
considered by the SDMS.
12
A Systemic Approach to Managing Natural Disasters
Climate Change
There is evidence that suggests that emissions of
greenhouse gases are already changing our climate
(Aalst, 2006; Black, 2006; Helmer & Hilhorst,
2006; Intergovernmental Panel on Climate Change
(IPCC), 2001; Trenberth, 2005); e.g., it is believed
that the global warning is the main cause of the
worsening of floods around manila Bay (Kelvin
et al., 2006).
National and Local Cultures
National and communities’ cultures on crisis and
response management should be considered by
the disaster management system, although caution
is needed to avoid simplistic and stereotypic
judgements. It may be argued that such behaviour
is likely to slow down response management and
consequently it may create time lags. The disaster
management system should take into account such
cultural behaviour when assessing risks associated
with, for example, an emergency response (Casse,
1982; Heath, 1995; Hofstede, 1980).
Learning from Past Disasters
Past disasters should be analyzed in order to learn
from them; i.e., to find out what went wrong and
what went right so that lessons can be incorporated
into the disaster management system. However,
there is evidence that shows that this issue has
not been addressed by local communities, governments,
etc.
Unplanned Urbanization
The complexity and sheer scale of humanity concentrated
into large cities creates a new intensity
of disaster risk. For instance, the fast and uncontrolled
growth of Mexico City with a population
of more than 20 million inhabitants is reflected in
dangerous construction of homes. In some areas
of Mexico City it is common place to see houses
built on steep hillsides (British Broadcasting
Corporation [BBC], 2006a, March 16).
Improper Construction of Buildings
Another contributing factor in disasters is related
to the materials and methods used to build homes
and other buildings. Very often in developing
countries public and private buildings are built
without taking into account potential hazards.
The above highlights the need on inherently
safer design houses against natural hazards and
these issues should be considered by the disaster
management system.
Technology
Technology is bound to affect organization’s
disaster management systems since there are usually
safety implications. The technology related
Table 3. Some ‘environmental’ factors that should be considered by the SDMS
External factors that may influence the performance of the SDMS
Climate change
National & local cultures
Learning from past disasters
Unplanned urbanization
Improper construction of buildings
Technology
Weather conditions after a disaster
Geographical location and settlements
Poverty
Cities in a continuous change
Lack of regulations
Isolation & remoteness
Armed conflicts
Epidemics
Politics
Corruption
Other
A brief description of each of the above is presented in the subsequent paragraphs.
13
A Systemic Approach to Managing Natural Disasters
to tsunami early warning systems has already
existed such as the website http://www.prh.noaa.
gov/ptwc. However, the countries from the Indian
Ocean lacked of such systems and were unable
to prevent the tsunami disaster in 2004 (UNDP,
2005). This should be considered by the disaster
management system.
Weather Conditions After a Disaster
The weather conditions may affect the relief efforts
after an natural hazard and this may escalate into
disaster. For instance, heavy rain and snowfall
hampered relief efforts in Kashmir, where three
million people were left homeless by the South
Asian earthquake in 2005; roads were closed and
helicopters grounded by bad weather and landslides.
In addition, survivors’ tents were flooded
and these made the communities vulnerable to
disaster (BBC, 2006b, April 8).
Geographical Locations
and Settlements
The geographical location of cities may contribute
to disasters; i.e., those that have been founded in
highly hazardous locations. For instance, the city
of Lima, Peru, was founded in an area of very high
seismicity; the city has been severely damaged
by earthquakes, such as those that occurred in
1966 and 1970 (McEntire & Fuller, 2002). More
recently, the flooding of the city of New Orleans,
US, due to Hurricane Katrina in 2005 illustrates
the inappropriate location of settlements (Jackson,
2005). On the other hand, when the population
expands faster than the capacity of city authorities
or the private sector can supply housing or basic
infrastructure, informal settlements can explode.
For example, some 50% to 60% of residents live in
informal settlements in Bogota (Colombia), Bombay
and Delhi (India), Buenos Aires (Argentine),
Lagos (Nigeria), and Lusaka (Zambia). Similarly,
60% to 70% in Dar Es Salaam (Tanzania) and
Kinshasa (DR Congo); and more than 70% in Addis
Ababa (Ethiopia), Cairo (Egypt), Casablanca
(Morocco) and Luanda (Angola) (United Nations
Human Settlements Programme [UN-HABITAT],
2006). The above highlights the vulnerability of
these cities to disasters.
Poverty
Poverty may be another factor that contributes to
disaster risk. Moser (1998) argues that disaster
risk in cities is shaped by greater levels of social
exclusion and the market economy. Social exclusion
is associated to the high number of migrants
to a city where they are exposed at high risk from
disaster.
Cities in a Continuous Change
Cities may be regarded as complex systems which
are in a continuous change. They transform their
surroundings and hinterlands and these processes
may generate and create new hazards. For instance,
the destruction of mangroves in coastal areas may
increase hazard associated with ‘storm surge’; the
urbanisation of watershed through settlement, land
use change and infrastructure development may
contribute to the increase of flood and landslide
hazard; see for example, Zevallos (1996).
Lack of Regulations
Very often in developing countries, governments
have been ineffective in regulating the process of
urban expansion through both land-use planning
and building codes. Unregulated low income
settlements are the most hazard prone areas;
low building standards may be reflect a lack of
control, supervision, resources in order to build
resistant structures in such areas. It may be argued
that hazard prone areas are often preferred by the
poor because they may gain greater accessibility
to urban services and employment, even though
natural hazard risk may be increased. For example,
in central Delhi (India), a squatter settlement in
14
A Systemic Approach to Managing Natural Disasters
the flood plain of the Yemura River has been inhabited
for more than 25 years (Sharma & Gupta,
1998; UNDP, 2004).
Isolation and Remoteness
Deficient rural infrastructure and its vulnerability
to natural hazards can increase livelihood risks
and food insecurity in isolated communities. For
instance, the Neelum valley with an estimated
160 000 inhabitants was cut off from the rest of
Pakistani-administered Kashmir and became one
of the most inaccessible areas hit by the South
Asian earthquake in 2005. The mountain people
of the valley are dependent on roads; however, the
massive landslides at the valley entrance made it
completely dependent on helicopters for supplies
(BBC, 2006b, April 8).
Armed Conflicts
According to the UNDP (2002) Human Development
Report, during the 1990s a total of 53 major
armed conflicts resulted in 3.0 million deaths
which nearly 90% are believed to be civilians. In
2002, there were approximately 22 million international
refugees in the world and another 20 to
25 million internally displaced people. The fact of
being a refugee or an internally displaced person
raises vulnerability. When the displaced settle in
squatter settlements in cities, very often they are
exposed to new hazards because dangerous locations
where they can find shelter. For example,
Afghanistan suffered three years of drought and
a major earthquake on top of decades of armed
conflict, creating a particularly acute humanitarian
crisis (UNDP, 2002).
Epidemics
Epidemic diseases may be seen as disasters in
their own right but they also interact with human
vulnerability and natural disasters. Following a
disaster, for example, the population is influenced
by the type of hazard and the environmental
conditions in which it takes place, the particular
characteristics of those people exposed to the disaster
and their access to health services. Natural
hazard events, such as, flooding or temperature
increase in highland areas can extend the range
of ‘vector-born’ diseases such as malaria. In El
Salvador, for example, local health centres were
destroyed by an earthquake in the year 2002; as
a result, people had to travel for hours to reach
medical care. Despite the arsenal of vaccines and
drugs that exist today, infectious diseases are on the
increase, particularly in the developing countries
(UNDP, 2002)
Politics
Politics also contributes to disasters. McEntire
& Fuller (2002) argue that the concentration
of political power may have limited the capacity
of local leaders and emergency managers to
undertake the steps they felt were necessary to
prevent calamity in Peru. For instance, officials
in the city and department of Ica asked the central
government as early as November 1997 to take
preventive measures or release funds, so potential
hazards could be addressed locally but this plea
was denied or ignored by the government (La
Fernandez, 1998a, February 3). However, when
the full strength of El Niño arrived a few months
later, Ica was largely unprepared to deal with such
event. The centralization of decision making was
regarded as one of the main reasons why the city
of Ica was devastated by the severe floods on 30
January 1998. Similar problems were evident in
other parts of the country as well (Fernandez,
1998b, February 5; McEntire & Fuller, 2002).
Corruption
Humanitarian relief is often needed in countries
which are usually corrupt. The risk of aid diversion
is high and very often occurs at any point in
the response by any or all of the actors involved
15
A Systemic Approach to Managing Natural Disasters
in: donor contracting, public fundraising, by national
officials, UN staff, international NGO (Non
Governmental Organizations) and local NGOs,
and by recipients themselves (Willitts-King &
Harvey, 2005). The term “corruption” is used as
a shorthand reference for a large range of illicit or
illegal activities. Although, there is no universal
or comprehensive definition as to what constitutes
corrupt behaviour, the most prominent definitions
share a common emphasis upon the abuse of
public power or position for personal advantage.
Corruption can thrive in times of disaster and
when it is already entrenched, the possibilities
for abusing emergency aid are even greater. For
instance, the province of Aceh is among Indonesia’s
wealthiest in terms of natural resources; it
is also widely considered one of the most corrupt
provinces in Indonesia. It is believed that extortion
is being reported to be rampant across the
province, especially on main highways and carried
out almost entirely by the military (TNI) and the
police (Clark et al., 2005). It has been reported that
TNI was selling freely donated food to homeless
people immediately after the 2004 tsunami disaster
(James, 2006). Indonesian Corruption Watch said
that bureaucrats were reselling donated rice in
Aceh and aid supplies were been pilfered before
arriving in Banda Aceh (James, 2006).
It should be pointed out that most of the factors
mentioned above overlap and the order given is not
meant to imply any kind of order of importance
but it is simply a list of some of the factors which
might be considered by the SDMS. Other factors
may also be relevant.
fuTure researCh direCTions
A Systemic Disaster Management System (SDMS)
has been presented. The SDMS aims to maintain
disaster risk within an acceptable range whatever
that might be in the operations of any organization
(country, community, etc.) in a coherent way. The
future research includes:
1. The numerical assessment of the effectiveness
of the SDMS model by employing the
concept of viability. Viability has been defined
as the probability that the SDMS will
be able to maintain disaster risk within an
acceptable range for a given period of time
(see Table 1).
2. To apply the model to the analysis of past
natural disasters such as the following:
a. The Mexico City earthquake. On
September 19, 1985, at 7:19 local time,
an earthquake with a magnitude of 8.1
on the Richter scale struck Mexico’s
Capital City. It is believed that more
than 10,000 people were killed, 30,000
were injured, and large parts of the city
were destroyed. It is thought that about
6,000 buildings were flattened and a
quarter of a million people lost their
homes. The Mexico City earthquake is
being regarded as the most catastrophic
in the country’s history (Pan American
Health Organization [PAHO], 1985).
b. The Tabasco’s flood disaster. On
November 2007, torrential rains caused
the worst flooding in the southern
Mexican state of Tabasco in more than
50 years. It is believed that more than
one million people were affected. Some
preliminary results have been presented
in Santos-Reyes and Beard (2009).
c. The Tsunami disaster. On 26 December
2004 the biggest earthquake in 40 years
occurred between the Australian and
Eurasian plates in the Indian Ocean.
The quake triggered a tsunami; i.e., a
series of large waves that spread thousands
of kilometres over several hours.
It is believed that the disaster left at
least 165,000 people dead, more than
half a million more were injured and
up to 5 million others in need of basic
services and at risk of deadly epidem16
A Systemic Approach to Managing Natural Disasters
ics in a dozen Indian Ocean countries
(UNDP, 2005).
These cases may help to illustrate some of the
features of the model such as:
a. The possible advantages or disadvantages of
the concept of relative autonomy (RA). That
is, RA may have the advantage in terms of
helping to make local organizations more
effective; e.g., in helping to try to get the
message to the people ‘on the ground’. On the
other hand, it may be problematic if the local
organization is corrupt, or ‘incompetent’. In
that case, it would be better to have a strong
control from outside (i.e systems 2-5), to try
to ensure the effective implementation of
safety policies.
b. The need for a direct channel of communication
from the NDO to System 4* (i.e., the
confidential reporting system, see Figure
1); that is, avoiding the need for people
‘on the ground’ to always go through the
Management Units (e.g., LDMU; see Figure
1), especially as a person ‘on the ground’
may be complaining about the LDMU (e.g.,
because of ‘corruption’ or ‘incompetence’ or
nepotism or partiality).
c. The decomposition of System 2. In the present
application, System 2 has been broken
into NEWCC (National Early Warning
Coordination Centres) and LEWCC (Local
Early Warning Coordination Centres).
However, it is not clear how the decomposition
of System 2 might be at the next higher
level of recursion; i.e., at international level.
The analysis of the tsunami disaster may
help to illustrate this.
d. The channels of communication’s effectiveness
or lack of it. It has long been known
that an organization’s communication system
has a significant impact on the organization’s
performance. Moreover, multiple distributed
decision-making may be impossible without
communication. The ‘Four principles of organization’
and the ‘Paradigms’ (see Table 1)
which are intended to give essential features
for effective communication and control
may help to illustrate the above.
ConClusion
The natural disasters described briefly in the introduction
section have highlighted that the existing
approaches to the management of disaster risk may
be inadequate in dealing with such catastrophic
events. In addition, they have elucidated the need to
improve radically the performance of the existing
‘disaster management systems’. A great deal of
effort has been made, by academe, international
organizations, and governments, practitioners,
to investigate and develop approaches to address
disaster risk. However, the approaches reviewed
in the background section may represent a step
forward to managing disaster risk but may not
be enough to address the management of natural
disasters effectively. Furthermore, it may be argued
that they still tend to address disaster risk from an
‘isolation’ point of view and this will ultimately
fail to fundamentally understand the nature of risk
(Beard, 1999; Santos-Reyes & Beard, 2001). That
is, the cause of a natural disaster may be found
in the complexity of the relationships implicit
in the physical location of the settlements, the
design of the houses, communication systems,
Early Warning Systems (EWSs), national infrastructure,
climate change, etc. These have been
recognised by some researchers, such as McFadden
(Kettlewell, 2005a, January 6), who argues
that: “there’s no point in spending all the money
on a fancy monitoring and a fancy analysis system
unless we can make sure the infrastructure for the
broadcast system is there….that’s going to require
a lot of work. If it’s a tsunami, you’ve got to get
it down to the last Joe on the beach. This is the
stuff that is really very hard”. Similarly, McGuire
(Kettlewell, 2005b, March 25) argues that: “I have
17
A Systemic Approach to Managing Natural Disasters
no doubt that the technical element of the warning
system will work very well but there has to be an
effective and efficient communications cascade
from the warning centre to the fisherman on the
beach and his family and the bar owners”. In order
to gain a full understanding and comprehensive
awareness of disaster risk in a given situation it
is necessary to consider in a coherent way all the
aspects that may contribute to natural disasters.
In short, there is a need for a systemic approach
to natural disasters management. Systemic means
looking upon things as a system; systemic means
seeing pattern and inter-relationship within a
complex whole; i.e., to see events as products of
the working of a system. System may be defined
as a whole which is made of parts and relationships.
Given this, ‘failure’ may be seen as the
product of a system and, within that, see death/
injury/property losses and losses to the economy
as results of the working of systems.
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Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibite
A Systemic Approach to
Managing Natural Disasters
Jaime Santos-Reyes
SEPI-ESIME, IPN, Mexico
Alan N. Beard
Heriot-Watt University, Scotland
inTroduCTion
Natural disasters may be defined as events that
are triggered by natural phenomena or natural
hazards (e.g., earthquakes, hurricanes, floods,
windstorms, landslides, volcanic eruptions and
wildfires). Throughout history, natural disasters
have exerted a heavy toll of death and suffering
and are increasing alarmingly worldwide. During
the past two decades they have killed millions of
people, and adversely affected the life of at least
one billion people. For example, recent disasters,
such as the quake that triggered a tsunami in the
Indian Ocean (United Nations Development Programme
[UNDP], 2005); earthquake in Pakistan
(Kamp et al., 2008); the Wenchuan earthquake in
China (Zhao et al., 2009) and more recently the
L’Aquila earthquake in Italy (Owen & Bannerman,
2009). On the other hand, hurricanes have
shown how vulnerable coastal communities could
be to such events. For instance, Hurricane Katrina
caused an estimated $35 to $60 billion in damage
aBsTraCT
The objective of this chapter is to present a Systemic Disaster Management System (SDMS) model. The
SDMS model is intended to provide a sufficient structure for effective disaster management. It may be
argued that it has a fundamentally preventive potentiality in that if all the subsystems (i.e., systems 1-5)
and channels of communication are present and working effectively, the probability of failure should
be less than otherwise. Moreover, the model is capable of being applied proactively in the case of the
design of a new ‘disaster management system’ as well as reactively. In the latter case, a past disaster
may be examined using the model as a ‘template’ for comparison. In this way, lessons may be learned
from past disasters. It may also be employed as a ‘template’ to examine an existing ‘disaster management
system’. It is hoped that this approach will lead to more effective management of natural disasters.
DOI: 10.4018/978-1-61520-987-3.ch001
2
A Systemic Approach to Managing Natural Disasters
and resulted in at least 1000 deaths in the United
States alone. More recently, on November 2007,
the State of Tabasco, Mexico, has been flooded
and it has been regarded as one of the worst in
more than 50 years. It is believed that the disaster
left more than one million people homeless. Finally,
it is thought that 2008 has been one of the
most devastating years on record; i.e., more than
220,000 people have been killed in 2008 alone.
The above stresses the importance of prevention,
mitigation and preparedness including evacuation
planning in order to mitigate the impact of
natural disasters. Disaster prevention includes
all those activities intended to avoid the adverse
impact of natural hazards (e.g., a decision not to
build houses in a disaster-prone area). Mitigation,
on the other hand, refers to measures that
should be taken in advance of a disaster order to
decrease its impact on society (e.g., developing
building codes). Finally, disaster preparedness
includes pre- and post- emergency measures that
are intended to minimize the loss of life, and to
organize and facilitate timely effective rescue,
relief, and rehabilitation in case of disaster (e.g.,
organizing simulation activities to prepare for an
eventual disaster relief operation).
Given the above, natural disasters present
a great challenge to society today concerning
how they are to be mitigated so as to produce an
acceptable risk is a question which has come to
the fore in dramatic ways in recent years. As a
society we have tended to shift from one crisis to
another and from one bout of crisis management
to another. There is a need to see things in their
entirety, as far as we are able. In relation to disaster
management, it becomes vital to see disaster risk
as a product of a system; to have a ‘systemic’ approach.
Despite this, very little emphasis has been
given by academe, international organizations,
NGO (Non Governmental Organizations), and
practitioners as to what constitutes and defines
an effective disaster management system, both in
terms of structure and process, from a systemic
point of view. This chapter presents a Systemic
Disaster Management System (SDMS) model. The
model is intended to help to maintain disaster risk
within an acceptable range whatever that might
mean. The model is intended to provide a structure
for an effective disaster management system. It
may be argued that it has a fundamentally preventive
potentiality in that if all the sub-systems
and channels of communication are present and
working effectively, the probability of a failure
should be less than otherwise. It is hoped that this
approach will lead to more effective management
of natural disasters
BaCkground
A great deal of effort has been made, by academe,
international organizations, and governments,
practitioners, to investigate and develop approaches
to address disaster risk. For instance,
during the 1990s the United Nations (UN) sponsored
the International Decade for Natural Disaster
Reduction (IDNDR) with the aim of reducing
losses caused by natural hazards (Annan, 1988).
The IDNDR Scientific and Technical Committee
identified five challenges to guide future programs:
(1) Integrate natural disaster management with
overall planning; (2) anticipate mega disasters
due to population concentrations; (3) reduce environmental
and resource vulnerability; (4) improve
disaster prevention capabilities of developing
countries; and (5) assure effective coordination
and implementation. The UN has also established
the International Strategy for Disaster Reduction
(ISDR) which serves as an international information
clearinghouse on disaster reduction, developing
awareness campaigns and producing articles,
journals, and other publications and promotional
materials related to disaster reduction; the publication
of “Living with risk: A global review of
disaster reduction initiatives” document (ISDR,
2004) is an example of these.
Other world organizations and countries have
published a vast amount of reports and publica3
A Systemic Approach to Managing Natural Disasters
tions on the management of disasters; inter alia,
(Colombo & Vetere Arellano, 2002; ECLAC,
1991; Freeman et al., 2002; Jayawardane, 2006;
Kazusa, 2006; Kreimer & Arnold, 2000). Other
authors, such as Vakis (2006) discusses natural
disasters within the general framework of ‘social
risk management’ and highlights the complementary
role that “social protection” can play in the
formation and response of an effective strategy for
natural disasters management system. The author
proposes a number of “social protection” issues
that can be used in practice to address natural
disasters. On the other hand, it is now recognised
that ‘development’ and disasters have a close and
complex relationship. For instance, Mileti et al.
(1995) argue that “losses from natural disasters
occur because of development that is unsustainable”.
Similarly, Stenchion (1997) emphasises
that “development and disaster management are
both aimed at vulnerability reduction”. Some
authors, such as Cuny (1994) argues that development
is often set back by disasters and others
assert that post-disaster operations should take
into account a development perspective (see also
Berke et al., 1993; McAllister, 1993). The United
Nations Development Programme published the
document “Reducing disaster risk: A challenge
for development” (UNDP, 2004). The report in
a way summarizes the above points; i.e., natural
disaster risk is connected to the process of human
development and that disasters put development
at risk. Furthermore, it emphasizes that human
development can also contribute to reduction in
disaster risk. Finally, the report argues that disaster
risk is not inevitable and offers examples of good
practice in disaster risk reduction that can be built
into ongoing development planning policy.
Other researches have concentrated on several
issues regarding disaster management; i.e., organizational,
technological, early warning systems,
economic, emergency, etc. For instance, Granot
(1997) reviews the diverse cultures of different
organizations and a number of findings regarding
emergency services and suggests directions that
may improve inter-organizational relationships.
Kouzmin et al. (1995), on the other hand, discusses
the efficiency of disaster management policies and
programmes in Australia. The authors argue that
there are longstanding deficiencies in strategic and
operational planning and forecasting approaches;
they argue the need for more co-operation and
co-ordination between the various emergency
services, and finally, the authors discuss the development
of terrestrial and space technologies
which could be used in disaster management.
Other authors have concentrated their research
on emergency response preparedness issues. For
example, Wilson (2000) examines small group
training for those in charged with responding in
an emergency situation. Wilson argues that to
ensure both effective and efficient training it is
important to understand that people learn in different
ways. Cosgrave (1996) proposes that decision
making is part of all management tasks and that it
is particularly important for emergency managers
as they often need to take decisions quickly. The
author reviews some of the particular problems
of emergency decision and looks at the usefulness
of Vroom and Yetton’s decision process model for
emergencies (Vroom & Yetton, 1973), before proposing
a simplified problem classification based on
three problem characteristics. Cosgrave concludes
by reviewing a collection of “emergency” decisions
and analysing some of the common factors
to suggest a number of simple action rules to be
used in conjunction with the proposed simplified
decision process model.
Fisher (1998) has investigated the role of
the new information technologies in emergency
mitigation, planning, response and recovery.
The author illustrates the utility of multimedia,
CD-ROM, e-mail and Internet applications to
enhance emergency preparedness. Technologies
such as ‘remote sensing’, GIS (Global Positioning
System) and GPS (Geographical Information
System), also known as ‘3S’ technology, have been
used in the process of monitoring disasters. Murai
(2006) has developed a system for monitoring
4
A Systemic Approach to Managing Natural Disasters
disasters using ‘remote sensing’, GIS and GPS.
The author argues that the developed monitoring
system records the real status of damages due to
natural disasters and analyzes the “cause” of a
disaster and predicts its occurrence. Following the
tsunami disaster in 2004, the General Secretary
of the United Nations (ONU) Kofi Annan called
for a global early warning system for all hazards
and for all communities. He also requested the
ISDR and its UN partners to conduct a global
survey of capacities, gaps and opportunities in
relation to early warning systems (Annan, 2005).
The produced report, “Global Survey of Early
Warning Systems”, concluded that there are many
gaps and shortcomings and that much progress
has been made on early warning systems and
great capabilities are available around the world
(Egeland, 2006). However, it is argued here that it
may be not enough to have such systems without
concentrating on ‘wider’ issues, such a system
where an EWS may be just part of it.
More recently, there has been considerable
interest on the concepts of vulnerability and resilience.
However, there are multiple definitions
of these two concepts in the literature and there
is not an accepted definition (Klein et al., 2003;
Manyena, 2006). For instance, Cutter et al. (2008)
defines vulnerability as the “pre-event, inherent
characteristics or qualities of social systems that
create the potential for harm”. On the other hand,
numerous frameworks, conceptual models, and
vulnerability assessment techniques have been
developed in order to address the theoretical
underpinnings and practical applications of vulnerability
and resilience (Adger, 2006; Burton
et al., 2002; Eakin & Luers, 2006; Fussel, 2007;
Gallopin, 2006; Green & Penning-Rowsell, 2007;
Klein et al., 2003; McLaughlin & Dietz, 2008;
Polsky et al., 2007).
a sdMs Model
The Systemic Disaster Management System
(SDMS) model is intended to maintain disaster
risk within an acceptable range in an organization’s
operations in relation to disaster management.
It may be argued that if all the sub-systems
and channels of communication and control are
present and working effectively, the probability
of a failure should be less than otherwise; in this
sense the model has a fundamentally preventive
potentiality. Table 1 summarizes the main char-
Table 1. Fundamental characteristics of the SDMS model
1 A recursive structure (i.e., ‘layered’) and relative autonomy (RA)
2 A structural organization which consists of a ‘basic unit’ in which it is necessary to achieve five functions associated with systems 1
to 5. (See Figure1).
(a) system 1: disaster-policy implementation
(b) system 2: disaster- national early warning coordination centre (NEWCC)
(c) system 2*: disaster-local early warning coordination centre (LEWCC)
(d) system 3: disaster-functional
(e) system 3*: disaster-audit
(f) system 4: disaster-development
(g) system 4*: disaster-confidential reporting system
(h) system 5: disaster-policy
Note: whenever a line appears in Figure 1 representing the SDMS model, it represents a channel of communication.
3 The SDMS & its ‘environment’
4 The concept of MRA (Maximum Risk Acceptable), Viability and acceptable range of risk.
5 Four principles of organization
6 ‘Paradigms’ which are intended to act as ‘templates’ giving essential features for effective communication and control.
5
A Systemic Approach to Managing Natural Disasters
acteristics of the model and Figure 1 shows the
structural organization of the SDMS model.
recursive structure of
the sdMs Model
A Recursion may be regarded as a ‘level’, which
has other levels below or above it. The concept
of recursion is intended to help to identify the
level of the organization being modelled or being
considered for analysis. Figure 2 is intended to
show three levels of recursion for an organization.
System 1 at level 1 contains the sub-system of
interest; i.e., the ‘National Disaster Operations’
(NDO) which may be taken to be the highest level
of the system of interest (e.g., level of a country).
The sub-system is represented as an elliptical
symbol that contains two essential elements:
1. The ‘National Disaster Management Unit’
(NDMU) represented by a parallelogram
symbol which is concerned with the ‘disaster
risk management’ in the ‘National Disaster
Operations’ (NDO) of the organization, and
2. The NDO, which is where the disaster risks
are created, within system 1, due to the interaction
of all the processes that take place
within a country, region or community. There
may be other risks due to interaction with
the ‘environment’ (see section ‘the SDMS
& its environment’ for further details about
these). Note that the double arrow line connecting
(1) & (2) represent the managerial
interdependence.
Increasing the level of resolution of the system
of interest, i.e., NDO at one level below recursion
Figure 1. A SDMS model
6
A Systemic Approach to Managing Natural Disasters
1 will result in the ‘Zone A-Disaster Operations’
(ZADO) & ‘Zone B-Disaster Operations’ (ZBDO)
and this is shown at level 2 in Figure 2. It must be
pointed out that each of these sub-systems can be
de-composed into further sub-systems depending
on our level of interest. For example, ‘Region-1
Disaster Operations’ (R1DO), ‘Region-2 Disaster
Operations’ (R2DO) and ‘Region-3 Disaster
Operations’ (R3DO) are shown as sub-systems
of the ‘Zone A Disaster Operations’ (ZADO) at
level 3. In principle, each sub-system that forms
part of system 1 at level 3 can be de-composed
further depending on the level of interest of the
‘disaster management system’ modeller or analyst.
relative autonomy (ra)
The SDMS is intended to be able to maintain
disaster risk within an acceptable range at each
level of recursion, but this safety achievement,
at each level, is conditional on the cohesiveness
of the whole organization. The SDMS contains
a structure that favours relative autonomy and
local safety problem-solving capacity. Relative
autonomy means that each operation of system
1 of the SDMS is responsible for its own activity
with minimal intervention of systems 2-5. The
organizational structure of the SDMS allows
decisions to be made at the local level. Decision
making is distributed throughout the whole or-
Figure 2. Recursive structure of the SDMS
7
A Systemic Approach to Managing Natural Disasters
ganization. This means that distributed decision
making involves a set of decision makers in each
operation of system 1 and at each level of recursion.
These decision makers should be relatively
autonomous in their own right and act relatively
independently based on their own understanding of
safety and their specific tasks. However, it should
be recognised that they have interdependence
with other decision makers of other operations
of system 1 (see Figures 3 & 4). Therefore, each
operation of system 1 should be endowed with
relative autonomy so that the organizational safety
policy can be achieved more effectively. Relative
autonomy must not be confused with isolation;
it must be within an adequate system of control
and communication.
sTruCTural organizaTion
of The sdMs Model
The structural organization of the SDMS model
consists of a ‘basic unit’ in which it is necessary
to achieve five functions associated with systems
1 to 5. Systems 2 to 5 facilitate the function of
system 1, as well as ensuring the continuous adaptation
of the disaster management system as a
whole. The operations identified at recursion 2 (see
Figure 2) have been represented in the format of
Figure 3. Disaster management system-in-focus at recursions 1&2
8
A Systemic Approach to Managing Natural Disasters
the structural organization of the model. Figure 3
shows what is called here ‘disaster management
system-in-focus’ at recursions 1&2; similarly,
Figure 4 illustrates the ‘disaster management
system-in-focus’ at recursions 2&3. It should be
emphasized that both Figures should be seen in
the context of Figure 2. Referring to Figures 1&3:
system 1: disaster- Policy
implementation
System 1 may be regarded as the core of the SDMS
model. That is, it is where all the daily activities
within an organization (i.e., country, region, community,
etc.) take place and therefore, it is where
disaster risks are created. How system 1 might
be broken down is a key question; for example,
it might be de-composed on a basis of geography
or functions. For the purpose of the present case
system 1 has been de-composed on a basis of
geography as shown in Figures 2, 3&4.
As illustrated in Figure 1, system 1 is interrelated
with systems 2, 3&3*; i.e., system 1 consists
of several subsystems or operations, such
as ZADO, ZBDO, etc. Table 2 presents some
examples of the information that flows through
these channels of communication.
system 2: disaster- national
early Warning Coordination
Centre (neWCC)
The function of system 2 is to co-ordinate the
activities of the operations of system 1. System
Figure 4. Disaster management system-in-focus at recursions 2&3
9
A Systemic Approach to Managing Natural Disasters
Table 2. Examples of the sort of information that flows through the channels of communication
Communication channel
(see Figure 1) Description/Examples
System
1
System 1 to System 2
channel
Information about the maintenance programmes of physical infrastructure, such as early warning
systems; training programme of evacuation of the population, etc.
System 1 to System 3
channel
Information about: the lack of maintenance of the physical infrastructure; compliance and enforcement
of the legal and regulatory requirements; lack of forecasting systems; the need of new methodologies
for disaster risk identification, analysis and evaluation; the need to improve technologies, for
example, to control flood, etc.
System 1 to System 3*
channel
Compliance of public and private buildings with codes and standards as well as with land use plans;
whether the planned performance associated with the population’s response to an emergency (e.g.,
the effective response of the people, fire-fighters and police in an exercise based on the scenario of
an earthquake occurring) is being achieved or not.
System
2
System 2 to System 1
channel
A wide range of stakeholders need to be coordinated in the operations of system 1; for instance at
government level this means ensuring cross-departmental co-ordination; across society as a whole it
requires better links between the NGOs, the private sector and academia, etc. Coordination amongst
the main actors involved in the early warning chain to provide optimum conditions for informed
decision-making and response actions.
System 2 to System 3
channel
Malfunctioning or failure of a local early warning system (EWS); deficiencies on the channels of
communication between forecast and the intended recipient; i.e., the people from the communities, etc.
System
2*
System 2* to System 1
channel
Monitoring of data related to any particular sensor system; e.g., ocean bottom pressure sensors buoys,
tide gauging, etc. The communications may be achieved via wire line, wireless, satellite, etc.
System 2* to System 2
channel
If a deviation from an accepted criterion occurs then this is reported quickly to system 2.
System
3
System 3 to System 1
channel
Resource allocation for disaster reduction; i.e., financial, human, technical, material; legal and regulatory
requirements; i.e., laws, acts, regulations, codes, standards. For example, national disaster risk
reduction policies; standards (e.g., public and private building codes and standards); education and
training programmes: e.g., inclusion of disaster reduction at all levels of education (curricula, education
material), national and local training programmes; public awareness programmes, etc.
System 3 to System 2
channel
The performance of early warning systems,; the population’s awareness on how to react in case of
an earthquake, hurricane, etc.
System 3 to System 3*
channel
The population’s safety culture, etc.; the adequacy of the design and construction of public and private
houses; the adequacy of the training of evacuation programmes, etc.
System 3 to System 4
channel
System 3 communicates its needs to system 4; i.e., information about new developments on risk assessment
analysis techniques, new technologies, reassessment of process changes, new development
of means of escape, etc.
System
3*
System 3* to System 1
channel
Inadequacy of the design and construction of physical infrastructure; inadequacy of the critical infrastructure;
lack of maintenance of the physical infrastructure; deficiencies in the land use planning, etc.
System 3* to System 3
channel
Deficiencies in the design and construction of public and private houses; deficiencies of the population
on how to react in case of a natural hazard; i.e., an earthquake, etc.; lack of training of evacuation
programmes, etc.
System
4
System 4 to System 3
channel
Research programmes aiming to risk reduction; new methods in disaster risk identification and assessment;
new technologies aiming to improve the physical and technical measures, for example,
flood control techniques, soil conservation practices, retrofitting of building, etc.; modern methods
of monitoring, e.g., crop production, etc.
System 4 to System 5
channel
System 4 could, for example, communicate to system 5 about: the new technologies and regulations
related to the design of buildings identified in the ‘environment’; the development of new technologies
related to the prediction of earthquakes; new techniques in order to improve the flood control, etc.;
the development of new tools for risk assessments that reflect the dynamic nature of danger, such as,
climate change, urban growth, disease, etc.
10
A Systemic Approach to Managing Natural Disasters
2, along with system 1 management units, implements
the safety plans received from system 3.
It informs system 3 about routine information on
the performance of the operations of system 1. To
achieve the plans of system 3 and the needs of
system 1, system 2 gathers and manages the safety
information of system 1’s operations. Moreover,
it also coordinates other local early warning coordination
centres (LEWCCs).
As illustrated in Figure 1, system 2 is interrelated
with systems 1&3. Table 2 presents some
examples of the information that flows through
these channels of communication.
system 2*: disaster- local
early Warning Coordination
Centre (leWCC)
System 2* is part of system 2 and it is responsible
for communicating advance warnings to other
early warning coordination centres and to key
decision makers. This action is intended to help
to take appropriate actions prior to the occurrence
of a major natural hazard event. Santos-Reyes
(2007) gives some details about how this might
be achieved. Table 2 presents some examples of
the information that flows through these channels
of communication.
system 3: disaster- functional
(Monitoring, assessment)
System 3 is directly responsible for maintaining
risk within an acceptable range in system 1, and
ensures that system 1 implements the organization’s
safety policy. It achieves its function on a
day-to-day basis according to its own safety plans
and the strategic and normative safety plans received
from system 4. The purpose of these plans
is to anticipate and act proactively to maintain the
disaster risk, arising from the operations of the
sub-systems that form part of system 1.
As illustrated in Figure 1, system 3 is interrelated
with systems 1, 2, 3*& 4. Table 2 presents
some examples of the information that flows
through these channels of communication.
system 3*: disaster- audit
System 3* is part of system 3 and its function is to
conduct audits sporadically into the operations of
system 1. System 3* intervenes in the operations
of system 1 according to the safety plans received
from system 3. System 3 needs to ensure that the
accountability reports received from system 1
reflect not only the current status of the operations
of system 1, but are also aligned with the overall
objectives of the organization. The audit activities
should be sporadic (i.e., unannounced) and they
should be implemented under common agreement
between system 3* and system 1.
As illustrated in Figure 1, system 3* is interrelated
with systems 1&3. Table 2 presents some
examples of the information that flows through
these channels of communication.
system 4: disaster- development
System 4 is concerned with safety research and
development (R&D) for the continual adaptation
of the disaster management system as a whole.
By considering strengths, weaknesses, threats and
opportunities, system 4 can suggest changes to the
organization’s safety policies. This function may
be regarded as a part of effective safety planning.
System 4 achieves its function according to the
safety policy of system 5; i.e., to maintain disaster
risk within an acceptable range in the organizations
operations. System 4 should sense, scan and
attempt to respond appropriately to the various
threats and opportunities identified in the system’s
‘total environment’ (see Section ‘the SDMS &
Its environment’ for details of the environmental
factors). There are two main safety issues which
11
A Systemic Approach to Managing Natural Disasters
system 4 has to deal with regarding the ‘total
environment’. First, the large broken line elliptic
symbol represents the ‘total environment’ of the
system (see Figures 1, 3&4). Second, system 4
should deal with the ‘disaster future environment’.
The ‘disaster future environment’ is concerned
with threats and opportunities relating to future
development of safety that may be relevant for the
organization. Therefore, the SDMS deals not only
with current safety problems, but also anticipates
or prevents future disasters.
As illustrated in Figure 1, system 4 interacts
with the ‘total environment’, systems 5 &3. Table
2 presents some examples of the information that
flows through these channels of communication.
system 4*: disaster- Confidential
reporting system
System 4* is part of system 4 and is concerned
with confidential reports or causes of concern from
any employee, about any aspects, some of which
may require the direct and immediate intervention
of system 5. This means that system 4* analyses
all information coming through this channel and
develops and plans actions to act upon what has
been reported so that these or similar incidents
or causes of concern do not occur in the future.
system 5: disaster- Policy
System 5 is responsible for deliberating safety
policies and for making normative decisions. According
to alternative safety plans received from
system 4, system 5 considers and chooses feasible
alternatives, which aim to maintain disaster risk
within an acceptable range throughout the life
cycle of the total system. Furthermore, these safety
policies should: reflect the safety values and beliefs
of the whole organization; address the anticipation
of disasters due to natural hazard; promote safety
culture throughout the organization. System 5 also
monitors the interaction of system 3 and system
4, as represented by the lines that show the loop
between systems 3 and 4 as shown in Figures
1&3. The safety policies that are deliberated and
decided by system 5 for implementation should
address, for example, the following issues:
• It should also promote safety culture
throughout the organization.
• Establishment of policy in development
planning: e.g., poverty reduction or eradication,
social protection, sustainable development,
climate change ‘adaptation’,
natural resource management, health, education,
etc.
• Promotion of disaster risk awareness
through education at all levels of the
organization.
Hot-Line
Figures 1, 3 & 4 show a dashed line directly from
system 1 to system 5; it represents a direct channel
of communication or ‘hot-line’ for use in exceptional
circumstances; e.g., during an emergency.
It represents ‘initially’ one-way communication
channel but they may become two way communication
channels between systems 1 and 5.
The sdMs & its environment
‘Environment’ may be understood as those circumstances
to which the SDMS response is necessary.
‘Environment’ lies outside the SDMS but interacts
with it (see Figures 1, 3&4). It influences and is
influenced by the system. Thus, it is important to
consider it. For instance, natural hazards such as
earthquakes, hurricanes, etc, threaten the system;
so that these hazards and the associated risks should
be eliminated or controlled. In addition, table 3
lists some ‘environmental’ factors that should be
considered by the SDMS.
12
A Systemic Approach to Managing Natural Disasters
Climate Change
There is evidence that suggests that emissions of
greenhouse gases are already changing our climate
(Aalst, 2006; Black, 2006; Helmer & Hilhorst,
2006; Intergovernmental Panel on Climate Change
(IPCC), 2001; Trenberth, 2005); e.g., it is believed
that the global warning is the main cause of the
worsening of floods around manila Bay (Kelvin
et al., 2006).
National and Local Cultures
National and communities’ cultures on crisis and
response management should be considered by
the disaster management system, although caution
is needed to avoid simplistic and stereotypic
judgements. It may be argued that such behaviour
is likely to slow down response management and
consequently it may create time lags. The disaster
management system should take into account such
cultural behaviour when assessing risks associated
with, for example, an emergency response (Casse,
1982; Heath, 1995; Hofstede, 1980).
Learning from Past Disasters
Past disasters should be analyzed in order to learn
from them; i.e., to find out what went wrong and
what went right so that lessons can be incorporated
into the disaster management system. However,
there is evidence that shows that this issue has
not been addressed by local communities, governments,
etc.
Unplanned Urbanization
The complexity and sheer scale of humanity concentrated
into large cities creates a new intensity
of disaster risk. For instance, the fast and uncontrolled
growth of Mexico City with a population
of more than 20 million inhabitants is reflected in
dangerous construction of homes. In some areas
of Mexico City it is common place to see houses
built on steep hillsides (British Broadcasting
Corporation [BBC], 2006a, March 16).
Improper Construction of Buildings
Another contributing factor in disasters is related
to the materials and methods used to build homes
and other buildings. Very often in developing
countries public and private buildings are built
without taking into account potential hazards.
The above highlights the need on inherently
safer design houses against natural hazards and
these issues should be considered by the disaster
management system.
Technology
Technology is bound to affect organization’s
disaster management systems since there are usually
safety implications. The technology related
Table 3. Some ‘environmental’ factors that should be considered by the SDMS
External factors that may influence the performance of the SDMS
Climate change
National & local cultures
Learning from past disasters
Unplanned urbanization
Improper construction of buildings
Technology
Weather conditions after a disaster
Geographical location and settlements
Poverty
Cities in a continuous change
Lack of regulations
Isolation & remoteness
Armed conflicts
Epidemics
Politics
Corruption
Other
A brief description of each of the above is presented in the subsequent paragraphs.
13
A Systemic Approach to Managing Natural Disasters
to tsunami early warning systems has already
existed such as the website http://www.prh.noaa.
gov/ptwc. However, the countries from the Indian
Ocean lacked of such systems and were unable
to prevent the tsunami disaster in 2004 (UNDP,
2005). This should be considered by the disaster
management system.
Weather Conditions After a Disaster
The weather conditions may affect the relief efforts
after an natural hazard and this may escalate into
disaster. For instance, heavy rain and snowfall
hampered relief efforts in Kashmir, where three
million people were left homeless by the South
Asian earthquake in 2005; roads were closed and
helicopters grounded by bad weather and landslides.
In addition, survivors’ tents were flooded
and these made the communities vulnerable to
disaster (BBC, 2006b, April 8).
Geographical Locations
and Settlements
The geographical location of cities may contribute
to disasters; i.e., those that have been founded in
highly hazardous locations. For instance, the city
of Lima, Peru, was founded in an area of very high
seismicity; the city has been severely damaged
by earthquakes, such as those that occurred in
1966 and 1970 (McEntire & Fuller, 2002). More
recently, the flooding of the city of New Orleans,
US, due to Hurricane Katrina in 2005 illustrates
the inappropriate location of settlements (Jackson,
2005). On the other hand, when the population
expands faster than the capacity of city authorities
or the private sector can supply housing or basic
infrastructure, informal settlements can explode.
For example, some 50% to 60% of residents live in
informal settlements in Bogota (Colombia), Bombay
and Delhi (India), Buenos Aires (Argentine),
Lagos (Nigeria), and Lusaka (Zambia). Similarly,
60% to 70% in Dar Es Salaam (Tanzania) and
Kinshasa (DR Congo); and more than 70% in Addis
Ababa (Ethiopia), Cairo (Egypt), Casablanca
(Morocco) and Luanda (Angola) (United Nations
Human Settlements Programme [UN-HABITAT],
2006). The above highlights the vulnerability of
these cities to disasters.
Poverty
Poverty may be another factor that contributes to
disaster risk. Moser (1998) argues that disaster
risk in cities is shaped by greater levels of social
exclusion and the market economy. Social exclusion
is associated to the high number of migrants
to a city where they are exposed at high risk from
disaster.
Cities in a Continuous Change
Cities may be regarded as complex systems which
are in a continuous change. They transform their
surroundings and hinterlands and these processes
may generate and create new hazards. For instance,
the destruction of mangroves in coastal areas may
increase hazard associated with ‘storm surge’; the
urbanisation of watershed through settlement, land
use change and infrastructure development may
contribute to the increase of flood and landslide
hazard; see for example, Zevallos (1996).
Lack of Regulations
Very often in developing countries, governments
have been ineffective in regulating the process of
urban expansion through both land-use planning
and building codes. Unregulated low income
settlements are the most hazard prone areas;
low building standards may be reflect a lack of
control, supervision, resources in order to build
resistant structures in such areas. It may be argued
that hazard prone areas are often preferred by the
poor because they may gain greater accessibility
to urban services and employment, even though
natural hazard risk may be increased. For example,
in central Delhi (India), a squatter settlement in
14
A Systemic Approach to Managing Natural Disasters
the flood plain of the Yemura River has been inhabited
for more than 25 years (Sharma & Gupta,
1998; UNDP, 2004).
Isolation and Remoteness
Deficient rural infrastructure and its vulnerability
to natural hazards can increase livelihood risks
and food insecurity in isolated communities. For
instance, the Neelum valley with an estimated
160 000 inhabitants was cut off from the rest of
Pakistani-administered Kashmir and became one
of the most inaccessible areas hit by the South
Asian earthquake in 2005. The mountain people
of the valley are dependent on roads; however, the
massive landslides at the valley entrance made it
completely dependent on helicopters for supplies
(BBC, 2006b, April 8).
Armed Conflicts
According to the UNDP (2002) Human Development
Report, during the 1990s a total of 53 major
armed conflicts resulted in 3.0 million deaths
which nearly 90% are believed to be civilians. In
2002, there were approximately 22 million international
refugees in the world and another 20 to
25 million internally displaced people. The fact of
being a refugee or an internally displaced person
raises vulnerability. When the displaced settle in
squatter settlements in cities, very often they are
exposed to new hazards because dangerous locations
where they can find shelter. For example,
Afghanistan suffered three years of drought and
a major earthquake on top of decades of armed
conflict, creating a particularly acute humanitarian
crisis (UNDP, 2002).
Epidemics
Epidemic diseases may be seen as disasters in
their own right but they also interact with human
vulnerability and natural disasters. Following a
disaster, for example, the population is influenced
by the type of hazard and the environmental
conditions in which it takes place, the particular
characteristics of those people exposed to the disaster
and their access to health services. Natural
hazard events, such as, flooding or temperature
increase in highland areas can extend the range
of ‘vector-born’ diseases such as malaria. In El
Salvador, for example, local health centres were
destroyed by an earthquake in the year 2002; as
a result, people had to travel for hours to reach
medical care. Despite the arsenal of vaccines and
drugs that exist today, infectious diseases are on the
increase, particularly in the developing countries
(UNDP, 2002)
Politics
Politics also contributes to disasters. McEntire
& Fuller (2002) argue that the concentration
of political power may have limited the capacity
of local leaders and emergency managers to
undertake the steps they felt were necessary to
prevent calamity in Peru. For instance, officials
in the city and department of Ica asked the central
government as early as November 1997 to take
preventive measures or release funds, so potential
hazards could be addressed locally but this plea
was denied or ignored by the government (La
Fernandez, 1998a, February 3). However, when
the full strength of El Niño arrived a few months
later, Ica was largely unprepared to deal with such
event. The centralization of decision making was
regarded as one of the main reasons why the city
of Ica was devastated by the severe floods on 30
January 1998. Similar problems were evident in
other parts of the country as well (Fernandez,
1998b, February 5; McEntire & Fuller, 2002).
Corruption
Humanitarian relief is often needed in countries
which are usually corrupt. The risk of aid diversion
is high and very often occurs at any point in
the response by any or all of the actors involved
15
A Systemic Approach to Managing Natural Disasters
in: donor contracting, public fundraising, by national
officials, UN staff, international NGO (Non
Governmental Organizations) and local NGOs,
and by recipients themselves (Willitts-King &
Harvey, 2005). The term “corruption” is used as
a shorthand reference for a large range of illicit or
illegal activities. Although, there is no universal
or comprehensive definition as to what constitutes
corrupt behaviour, the most prominent definitions
share a common emphasis upon the abuse of
public power or position for personal advantage.
Corruption can thrive in times of disaster and
when it is already entrenched, the possibilities
for abusing emergency aid are even greater. For
instance, the province of Aceh is among Indonesia’s
wealthiest in terms of natural resources; it
is also widely considered one of the most corrupt
provinces in Indonesia. It is believed that extortion
is being reported to be rampant across the
province, especially on main highways and carried
out almost entirely by the military (TNI) and the
police (Clark et al., 2005). It has been reported that
TNI was selling freely donated food to homeless
people immediately after the 2004 tsunami disaster
(James, 2006). Indonesian Corruption Watch said
that bureaucrats were reselling donated rice in
Aceh and aid supplies were been pilfered before
arriving in Banda Aceh (James, 2006).
It should be pointed out that most of the factors
mentioned above overlap and the order given is not
meant to imply any kind of order of importance
but it is simply a list of some of the factors which
might be considered by the SDMS. Other factors
may also be relevant.
fuTure researCh direCTions
A Systemic Disaster Management System (SDMS)
has been presented. The SDMS aims to maintain
disaster risk within an acceptable range whatever
that might be in the operations of any organization
(country, community, etc.) in a coherent way. The
future research includes:
1. The numerical assessment of the effectiveness
of the SDMS model by employing the
concept of viability. Viability has been defined
as the probability that the SDMS will
be able to maintain disaster risk within an
acceptable range for a given period of time
(see Table 1).
2. To apply the model to the analysis of past
natural disasters such as the following:
a. The Mexico City earthquake. On
September 19, 1985, at 7:19 local time,
an earthquake with a magnitude of 8.1
on the Richter scale struck Mexico’s
Capital City. It is believed that more
than 10,000 people were killed, 30,000
were injured, and large parts of the city
were destroyed. It is thought that about
6,000 buildings were flattened and a
quarter of a million people lost their
homes. The Mexico City earthquake is
being regarded as the most catastrophic
in the country’s history (Pan American
Health Organization [PAHO], 1985).
b. The Tabasco’s flood disaster. On
November 2007, torrential rains caused
the worst flooding in the southern
Mexican state of Tabasco in more than
50 years. It is believed that more than
one million people were affected. Some
preliminary results have been presented
in Santos-Reyes and Beard (2009).
c. The Tsunami disaster. On 26 December
2004 the biggest earthquake in 40 years
occurred between the Australian and
Eurasian plates in the Indian Ocean.
The quake triggered a tsunami; i.e., a
series of large waves that spread thousands
of kilometres over several hours.
It is believed that the disaster left at
least 165,000 people dead, more than
half a million more were injured and
up to 5 million others in need of basic
services and at risk of deadly epidem16
A Systemic Approach to Managing Natural Disasters
ics in a dozen Indian Ocean countries
(UNDP, 2005).
These cases may help to illustrate some of the
features of the model such as:
a. The possible advantages or disadvantages of
the concept of relative autonomy (RA). That
is, RA may have the advantage in terms of
helping to make local organizations more
effective; e.g., in helping to try to get the
message to the people ‘on the ground’. On the
other hand, it may be problematic if the local
organization is corrupt, or ‘incompetent’. In
that case, it would be better to have a strong
control from outside (i.e systems 2-5), to try
to ensure the effective implementation of
safety policies.
b. The need for a direct channel of communication
from the NDO to System 4* (i.e., the
confidential reporting system, see Figure
1); that is, avoiding the need for people
‘on the ground’ to always go through the
Management Units (e.g., LDMU; see Figure
1), especially as a person ‘on the ground’
may be complaining about the LDMU (e.g.,
because of ‘corruption’ or ‘incompetence’ or
nepotism or partiality).
c. The decomposition of System 2. In the present
application, System 2 has been broken
into NEWCC (National Early Warning
Coordination Centres) and LEWCC (Local
Early Warning Coordination Centres).
However, it is not clear how the decomposition
of System 2 might be at the next higher
level of recursion; i.e., at international level.
The analysis of the tsunami disaster may
help to illustrate this.
d. The channels of communication’s effectiveness
or lack of it. It has long been known
that an organization’s communication system
has a significant impact on the organization’s
performance. Moreover, multiple distributed
decision-making may be impossible without
communication. The ‘Four principles of organization’
and the ‘Paradigms’ (see Table 1)
which are intended to give essential features
for effective communication and control
may help to illustrate the above.
ConClusion
The natural disasters described briefly in the introduction
section have highlighted that the existing
approaches to the management of disaster risk may
be inadequate in dealing with such catastrophic
events. In addition, they have elucidated the need to
improve radically the performance of the existing
‘disaster management systems’. A great deal of
effort has been made, by academe, international
organizations, and governments, practitioners,
to investigate and develop approaches to address
disaster risk. However, the approaches reviewed
in the background section may represent a step
forward to managing disaster risk but may not
be enough to address the management of natural
disasters effectively. Furthermore, it may be argued
that they still tend to address disaster risk from an
‘isolation’ point of view and this will ultimately
fail to fundamentally understand the nature of risk
(Beard, 1999; Santos-Reyes & Beard, 2001). That
is, the cause of a natural disaster may be found
in the complexity of the relationships implicit
in the physical location of the settlements, the
design of the houses, communication systems,
Early Warning Systems (EWSs), national infrastructure,
climate change, etc. These have been
recognised by some researchers, such as McFadden
(Kettlewell, 2005a, January 6), who argues
that: “there’s no point in spending all the money
on a fancy monitoring and a fancy analysis system
unless we can make sure the infrastructure for the
broadcast system is there….that’s going to require
a lot of work. If it’s a tsunami, you’ve got to get
it down to the last Joe on the beach. This is the
stuff that is really very hard”. Similarly, McGuire
(Kettlewell, 2005b, March 25) argues that: “I have
17
A Systemic Approach to Managing Natural Disasters
no doubt that the technical element of the warning
system will work very well but there has to be an
effective and efficient communications cascade
from the warning centre to the fisherman on the
beach and his family and the bar owners”. In order
to gain a full understanding and comprehensive
awareness of disaster risk in a given situation it
is necessary to consider in a coherent way all the
aspects that may contribute to natural disasters.
In short, there is a need for a systemic approach
to natural disasters management. Systemic means
looking upon things as a system; systemic means
seeing pattern and inter-relationship within a
complex whole; i.e., to see events as products of
the working of a system. System may be defined
as a whole which is made of parts and relationships.
Given this, ‘failure’ may be seen as the
product of a system and, within that, see death/
injury/property losses and losses to the economy
as results of the working of systems.
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Intercropping Corn with Lablab Bean, Velvet Bean, and Scarlet Runner Bean for Forage
Intercropping Corn with Lablab Bean, Velvet Bean, and Scarlet Runner Bean for Forage
1. Kevin L. Armstrong *a,
2. Kenneth A. Albrechtb,
3. Joseph G. Lauerb and
4. Heathcliffe Ridayc
+ Author Affiliations
1.
a Dep. of Crop Sciences, Univ. of Illinois, 1102 S. Goodwin Ave., Urbana, IL 61801
b 1575 Linden Dr., Dep. of Agronomy, Univ. of Wisconsin-Madison, Madison, WI 53706
c U.S. Dairy Forage Research Center, USDA-ARS, 1925 Linden Dr., Madison, WI 53706. This research was partially supported by funding through USDA Cooperative State Research Education and Extension Service (CSREES) Hatch Project WIS04802
Abstract
This experiment was designed to determine if intercropping corn (Zea mays L.) with climbing beans is a viable option to increase crude protein (CP) concentration in forage rather than purchasing costly protein supplements for livestock rations. In these experiments, corn was intercropped with three beans—lablab bean [Lablab purpureus (L.) Sweet], velvet bean [Mucuna pruriens (L.) D.C.], and scarlet runner bean (Phaseolus coccineus L.)—or grown in monoculture near Arlington and Lancaster, WI. Corn was sown in early May and late April in 2004 and 2005, respectively, and later thinned to 55,000 (low density) or 82,500 (normal density) plants ha−1 Beans were sown in rows 8 cm on one side of the corn rows at 82,500 plants ha−1 2 or 4 wk after corn planting. Averaged over four environments, mixture forage dry matter (DM) yields were similar. However the velvet bean and scarlet runner bean mixtures produced significantly higher forage DM yield, 1.2 Mg ha−1 and 0.89 Mg ha−1 more, respectively, in the late bean planting treatment. Beans, except scarlet runner bean, which was damaged by mold and insects, increased the CP concentration of all mixtures, with the greatest increases from the lablab bean (13%) and velvet bean (16%). The experiments show that lablab bean grown with corn has the greatest potential of the three beans to increase CP concentration above monoculture corn, without compromising forage yield or calculated milk ha−1 and increasing forage nutrient value.
Abbreviations
CP, crude protein;
DIP, degradable intake protein;
DM, dry matter;
IVTD, in vitro true digestibility;
NDF, neutral detergent fiber;
NDFd,;
NIRS, near infrared reflectance spectroscopy;
SECV, standard error of cross validation;
UIP, undegradable intake protein
Corn (Zea mays
L.) silage is a high-producing forage crop in the United States, with 2.4 million ha planted and 96.4 million Mg harvested in 2005 (USDA-NASS, 2006). Wisconsin was the top corn silage–producing state in the United States, with 13.6 million Mg harvested from 356,000 ha in 2005 (USDA-NASS, 2006). Corn silage is an important source of forage for dairy cattle in the United States because of its relatively consistent nutritive value, high yield, and high energy density compared with other forage crops (Coors and Lauer, 2001). One limitation to corn silage as a feed for dairy cows is low crude protein (CP) concentration; Darby and Lauer (2002) reported an average whole corn plant forage CP concentration of 73 g kg−1 across Wisconsin.
Because supplementing dairy rations with protein concentrate can be costly, alternatives have been explored. For example, Herbert et al. (1984) found intercropping corn and soybean [Glycine max (L.) Merr.] in alternating rows to be beneficial, with a 19 to 36% increase in CP concentration over monoculture corn with similar forage yields. Climbing beans have also been grown, with varying degrees of success, as an intercrop with corn. Kaiser and Lesch (1977) found that lablab bean [Lablab purpureus (L.) Sweet] increased CP concentration by 44% and lowered forage dry matter (DM) yield by 28%, with a lablab bean density of 108,000 plants ha−1 and a corn density ranging from 72,000 to 18,000 plants ha−1 They reported up to a 78% increase in CP concentration from monoculture corn at a density of 54,000 plants ha−1 to an intercrop density of corn at 18,000 plants ha−1 and a constant lablab bean density of 108,000 plants ha−1 Bryan and Materu (1987) found that intercropping cowpeas (Vigna unguiculata L. Walp) and corn increased CP concentration by 9% and did not lower forage DM yield compared with monoculture corn.
In Ghana, Haizel (1974) reported no difference in forage DM yield, harvested at full grain maturity, of monoculture corn or corn intercropped with cowpea in a good growing season. In Zimbabwe, Maasdorp and Titterton (1997) found varying results when intercropping climbing beans and corn. While lablab bean and scarlet runner bean (Phaseolus coccineus L.) did not produce much bean biomass in mixtures (less than 16%), velvet bean [Mucuna pruriens (L.) D.C.] accounted for nearly 30% of the intercrop mixture and depressed corn yield by 50%. The literature is inconclusive on the viability of such intercropping systems, and little information is available for production in cooler temperate regions.
This experiment was designed to determine if intercropping corn with beans is a viable option to increase CP concentration in forage rather than purchasing costly CP supplements for dairy cattle rations in a northern environment. The objectives of this research were to compare monoculture corn with corn–bean mixtures in terms of DM yield, nutritive value, potential milk production, and forage nutrient value and determine how these factors are affected by timing of bean planting and changes in corn density.
MATERIALS AND METHODS
Field experiments were conducted in 2004 and 2005 at the University of Wisconsin Agricultural Research Stations near Arlington (43°18′ N, 89°21′ W) and Lancaster (42°50′ N, 90°47′ W), WI. The experiments at Arlington were conducted on Plano silt loam (fine-silty, mixed, mesic Typic Argiudolls), on relatively flat and well-drained fields and at Lancaster on Rozetta silt loam (fine-silty, mixed, superactive, mesic Typic Hapludalfs), on a flat and well-drained field. The previous crop at Arlington was soybean both years. The previous crops at Lancaster were oat (Avena sativa L.) in 2004 and beans intercropped with corn in 2005. Before tillage, 170 and 135 kg ha−1 N as urea were applied at Arlington in 2004 and 2005, respectively. Before tillage, 180 kg ha−1 N as urea was applied at Lancaster in both years. Tillage operations included chisel plowing and field cultivation at Arlington and chisel plowing followed by a soil finisher at Lancaster in both years. Soil fertility levels at both locations were maintained at optimal levels for corn silage production (Kelling et al., 1998). Glyphosate [N-(phosphonomethyl)glycine] resistant corn hybrid DKC 50-20 was planted on 4 May 2004 and 25 Apr. 2005 at Arlington and 3 May 2004 and 26 Apr. 2005 at Lancaster. Corn was sown with 76.2-cm row spacing at 83,750 and 93,000 seeds ha−1 at both locations in 2004 and 2005, respectively, and later hand thinned to designated corn densities. Permethrin [(3- phenoxyphenyl) methyl(±)cis-trans 3-(2,2-dichloroethenyl)-2,2-dimethylcyclopropanecarboxylate] (6 g ha−1 a.i.), for control of corn wireworm [Melanotus communis (Gyll.)] and seed corn maggot [Delia platura (Meigen)], and carboxin (5,6-dihydro-2-methyl-N-phenyl-1,4-oxathin-3-carboxamide) (8 g ha−1 a.i.), for control of seed rots and decay, were applied at planting at Lancaster in both years. Glyphosate was spot sprayed for weed control at both locations, avoiding herbicide contact with the bean, along with hand weeding. Flumetsulam [N-(2,6-difluorophenyl)-5-methyl(1,2,4)triazolo (1,5-a)pyrimidine-2-sulfonamide] (28 g ha−1 a.i.) and s-metolachlor + safener [(1S)-2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide] (1.42 kg ha−1 a.i.) were applied pre-emergence to control weeds at both locations in 2005.
Experimental treatments included corn density, bean planting date, and species. The three bean species used were ‘Rongai’ lablab bean, velvet bean (speckled germplasm, from Sharad Phatak, University of Georgia), and ‘Scarlet Emperor’ scarlet runner bean. On the basis of a review of the literature, these bean species appear to have the best potential as intercrops with corn. Bean seeds were inoculated with appropriate rhizobia (Nitragin, Inc., Milwaukee, WI) and hand planted about 8 cm to one side of the corn rows at 2 or 4 wk after corn sowing. It was hypothesized that a delayed planting of beans alongside corn may be necessary to avoid excessive bean competition with corn development as noted by Maasdorp and Titterton (1997) When corn plants reached the V6 stage (Ritchie et al., 1992), they were hand thinned to a low density (55,000 plants ha−1) and a normal density (82,500 plants ha−1). Bean density was kept constant at 82,500 plants ha−1 It was hypothesized that a lower corn density would allow greater bean development and subsequently greater CP in harvested forage.
The experimental design was a randomized complete block design in 2004 and 2005 with four replications at each location. Treatments were the factorial combination of two bean sowing dates, two corn densities, and three bean species. There was a corn control. Experimental units consisted of three corn rows in 2004 and four corn rows in 2005 with associated bean rows in 8.2-m long plots. The middle corn row in 2004 and one of the middle rows in each experimental unit in 2005 were harvested for forage.
On the day of harvest, representative corn and bean plants were removed from selected harvest rows with treatments of normal corn density and early bean planting date for near infrared reflectance spectroscopy (NIRS) equation development to estimate bean and corn proportions in the mixtures. Separated corn and bean plants were dried at 60°C, weighed, and ground with a Christy hammer mill (Christy, Suffolk, UK) equipped with a 1-mm screen. Pure fractions (48 per experiment per year) and mixtures (36 per experiment per year) of corn and bean created by combining the pure fractions were used for NIRS equation development. Samples were scanned with a NIRSystems 6500 near infrared reflectance spectrophotometer (FOSS NIRSystems Inc., Eden Prairie, MN) equipped with a ring cup autosampler. Near-infrared reflectance spectra (1/R) were obtained between the wavelengths of 400 and 2498 nm. Data management and equation development were performed using WinISI 1.50 (Infrasoft International, Port Matilda, PA). Calibration statistics, [coefficient of determination [R
2] and standard error of cross validation [SECV]), for determining bean concentration in validation mixtures were SECV = 37 (R
2 = 0.99) and SECV = 20 (R
2 = 0.99) for separate equations developed in 2004 and 2005, respectively (Martens and Naes, 1989; Shenk and Westerhaus, 1991, 1994). Equations were used to predict bean concentration in mixtures and corn concentration was determined by subtracting bean concentration from total plot DM mass.
The middle row of each plot was harvested at the 50% kernel milk line stage (Afuakwa and Crookston, 1984) on 17 Sept. 2004 and 7 Sept. 2005 at Lancaster and 20 Sept. 2004 and 15 Sept. 2005 at Arlington. Harvest rows were chopped to a theoretical cutting length of 1 cm with a small, commercial forage harvester, and a 1-kg subsample was collected and dried at 60°C to determine DM concentration of the forage. The dried subsample was ground with a hammer mill equipped with a 1-mm screen.
Forages were analyzed for total N concentration by the Dumas method (AOAC, 1990) with an automated analyzer (LECO Model FP-528; LECO Corp., St. Joseph, MI). Crude protein concentrations were calculated by multiplying total N by 6.25. Neutral detergent fiber (NDF) concentrations were determined by the batch procedure outlined by ANKOM Technology Corp. (Fairport, NY). Subsamples (0.25 g each) were analyzed for in vitro true digestibility (IVTD) using rumen fluid from a lactating Holstein cow on a total mixed ration and buffer solution described by Goering and Van Soest (1970) with the Daisy II200 in vitro incubator and the ANKOM200 fiber analyzer (ANKOM Technology Corp., Fairport, NY). Neutral detergent fiber digestibility (NDFd) was calculated from the NDF and IVTD values as 100{[NDF − (100 − IVTD)]/NDF}. Ash concentration was determined by combustion of a 1.0-g subsample at 600°C for 2 h (data not shown but used in milk production models). Starch concentrations were determined by the procedures of Rong et al. (1996) and Owens et al. (1999)
Potential milk production estimates were calculated according to MILK2000 (Schwab et al., 2003). Milk per megagram DM and milk per hectare were calculated for corn and mixture forages. Values for ether extract were estimated from weighted values of corn silage (National Research Council, 2001) and lablab bean (Díaz et al., 2003) (depending on bean percentage in mixtures), while neutral detergent insoluble CP values were estimated from weighted values of corn silage and alfalfa (Medicago sativa L.) in the NRC tables (National Research Council, 2001).
Forage nutrient values were calculated with FEEDVAL4, a spreadsheet developed to assign a dollar value to feed ingredients (Howard and Shaver, 1997). The term forage nutrient value refers to the output of the spreadsheet, which allows the user to compare feeds based on current prices of feed ingredients and determine cost effectiveness. The FEEDVAL4 spreadsheet uses blood meal (undegradable intake protein, UIP), urea (degradable intake protein, DIP), shelled corn (energy), tallow (fat), dicalcium phosphate (phosphorus), and calcium carbonate (calcium) as reference feed ingredients. Prices for reference ingredients were based on April 2006 market values. The DM and CP components were measured values, while the total digestible nutrients of the mixtures were calculated using MILK2000. The UIP and DIP percentages of CP were estimated from a combination of corn silage and alfalfa values according to Linn et al. (1994), while the fat concentration was estimated from corn silage (National Research Council, 2001). Calcium and phosphorus concentrations for corn and beans were estimated from corn silage and alfalfa values from the NRC tables (National Research Council, 2001). Feed nutrient values were determined for 1 Mg of DM of each mixture and then multiplied by the corresponding mixture yield to provide crop value for 1 ha of each mixture.
Data from both years were pooled and analyzed as a randomized complete block design with the Windows version of SAS software package release 9.1 (SAS Institute, 2002). Tests concerning heterogeneity of variances were conducted to assess the appropriateness of pooling the data; however, no such problems existed in subsequent models. The MIXED procedure (SAS Institute, 2002) was used to detect treatment differences for the response variables of mixture yield, forage composition, milk per megagram and per hectare, and forage nutrient values. Corn density, bean planting date, and bean species were considered fixed effects, while years, locations, and blocks nested within locations were considered random effects. The Type 3 test of fixed effects was used to gauge significance at P < 0.05, and significant main effects were explored using the LSMEANS statement of SAS (SAS Institute, 2002). Fisher's protected LSD (P = 0.05) with no adjustment was used to compare mixture means, using the PDMIX800 macro (Saxton, 1998) when appropriate. Orthogonal contrast sets were also used to explore differences among significant (P < 0.05) interactions. The Pearson correlation coefficient, calculated in the CORR procedure (SAS Institute, 2002), was used to detect correlations between bean concentration in mixtures and mixture CP, NDF, IVTD, and starch concentrations.
RESULTS AND DISCUSSION
Environment and Crop Development
May 2004 was unusually cool and wet, delaying emergence and early seedling development of both corn and beans. Above-average precipitation occurred in May 2004 at both Arlington and Lancaster (Table 1
). The weather in May 2005 provided drier environments at both locations. In 2005, Arlington received less-than-normal precipitation in June and early July, so the site was irrigated with 51 mm of water on 28 June, 11 July, and 19 July to supplement rainfall to near-normal levels.
View Full Table | Close Full ViewTable 1.
Mean monthly precipitation and temperature at the Arlington and Lancaster Research Stations, WI, in 2004 and 2005 growing seasons.
Arlington
Lancaster
Month
Normal
2004
2005
Normal
2004
2005
Total monthly precipitation
mm
Apr.
82
48
20
85
37
54
May
87
261
85
92
280
68
June
103
104
88
120
123
169
July
98
110
214
107
101
140
Aug.
108
72
78
117
92
106
Sept.
93
13
119
81
2
76
Oct.
62
83
15
61
69
12
Total
633
691
619
663
704
625
Average monthly temperature
°C
Apr.
8
9
10
8
9
11
May
15
13
12
15
14
13
June
20
18
22
20
19
22
July
22
21
23
22
20
22
Aug.
21
18
21
21
18
21
Sept.
16
18
19
16
18
19
Oct.
10
10
10
10
10
11
Avg.
16
15
17
16
15
17
†
Normal precipitation and temperatures are based on 30-yr means.
‡
Precipitation includes 51 and 102 mm of irrigation in June and July.
Early bean growth in both years was variable among the different bean species. Scarlet runner bean emerged first in the early bean planting date and quickly grew to the height of the corn plants at stage V3 (Ritchie et al., 1992) by early June. In 2004, velvet bean seedlings emerged later and took several weeks to reach a full stand due to cool, wet soil, similar to the observations of Tracy and Coe (1918) Lablab bean emergence was intermediate, but seedlings were not as vigorous as scarlet runner bean. In 2005, velvet bean and lablab bean seedlings developed similarly in the drier spring. Once the weather was warmer, velvet bean began to grow better; however, much of its biomass was attributed to stems rather than leaves. Both scarlet runner bean and velvet bean produced some pods, but day length was too long for flower production in lablab bean. Velvet bean produced more biomass than did lablab bean in 2005 (data not shown); in addition, corn lodged due to the weight of the velvet bean biomass before harvest in the velvet bean plots. The difference in velvet bean performance between years was associated with cool and wet spring conditions in 2004 compared with 2005 (Tracy and Coe, 1918).
Forage Yield and Nutritive Value
Mixture forage DM yields were affected by corn planting density and were 17.2 and 21.0 Mg ha−1 for low and normal corn densities, respectively (Table 2
). The reduction in yield was associated with a 27,500 plants ha−1 difference between normal and low corn density treatments for which addition of beans did not compensate.
View Full Table | Close Full ViewTable 2.
Model significance levels, forage yield and nutritive value, and bean planting date and corn density effects of corn and bean mixtures pooled over four environments.
Total yield
Corn yield
Bean in mix
CP
NDF
IVTD
Starch
NDFd
Milk per megagram
Milk per hectare
Source of variation
P > F
Date
0.0023
0.0006
0.0389
NS
NS
NS
0.0333
NS
NS
0.0148
Density
<0.0001
<0.0001
<0.0001
<0.0001
0.0476
NS
0.0008
NS
NS
<0.0001
Date × density
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Species
0.0031
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0003
<0.0001
0.0002
Date × species
0.0346
0.0392
NS
NS
NS
NS
NS
NS
NS
NS
Density × species
NS
NS
0.0027
NS
NS
NS
NS
NS
NS
NS
Date × density × species
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Mixture effects
Species
Mg ha−1
g kg−1 DM
g kg−1 NDF
kg Mg−1
kg ha−1
Corn
19.1 ab
19.1 a
0 c
61 c
372 b
833 a
388 a
551 a
1810 a
34700 a
Lablab bean mix
19.6 a
17.5 bc
114 a
69 b
383 a
827 b
364 b
549 a
1780 b
35000 a
Scarlet runner bean mix
19.0 b
18.0 b
52 b
64 c
367 b
835 a
385 a
550 a
1810 a
34300 a
Velvet bean mix
18.7 b
16.9 c
103 a
71 a
389 a
818 c
360 b
533 b
1740 c
32600 b
Bean planting date and corn density effects
Bean planting date
Early
18.8 b
17.5 b
73 a
67
380
828
371 b
547
1790
33700 b
Late
19.4 a
18.2 a
62 b
66
375
829
378 a
544
1790
34700 a
Corn density
Normal
21.0 a
19.9 a
54 b
63 b
375 b
828
380 a
543
1780
37400 a
Low
17.2 b
15.9 b
81 a
70 a
381 a
828
368 b
548
1790
30900 b
†
CP, crude protein; NDF, neutral detergent fiber; IVTD, in vitro true digestibility; NDFd, neutral detergent fiber digestibility.
‡
NS, not significant.
§
Within columns, means followed by different letters are significantly different at P < 0.05.
Mixture forage DM yields were also affected by the interaction of bean planting date and bean species. Planting date had no effect on lablab bean–corn mixture yields; however, the velvet bean mixture yielded 1.2 Mg ha−1 more (P = 0.0011) and the scarlet runner bean mixture yielded 0.89 Mg ha−1 more (P = 0.0161) in the late planting date compared with the early planting date (Fig. 1
). In 2004, the early planted scarlet runner bean grew rapidly and became competitive with the corn plants early in the season but by harvest time contributed little because of potato leafhopper [Empoasca fabae (Harris)] and white mold [Sclerotinia sclerotiorum (Lib.) de Bary] damage. Lablab bean and velvet bean were not damaged by these pests. The later bean planting date did not have such a profound effect on yield because there was less early competition with corn. In 2005, the velvet bean mixture produced more with a late bean planting date because of greater bean biomass and corn lodging seen with the early bean planting date. In South Africa, Kaiser and Lesch (1977) reported a 7% DM yield reduction in a lablab bean–corn mixture compared with monoculture corn. Bryan and Materu (1987) did not find a significant difference between monoculture forage corn yield (8.8 Mg ha−1) and a corn–cowpea mixture yield (9.4 Mg ha−1) in West Virginia. Corn–bean mixture yields in the current Wisconsin experiments were approximately double those reported by Kaiser and Lesch (1977) and Bryan and Materu (1987), suggesting that environmental conditions were not optimal for corn production in the earlier experiments.
Figure 1.
Effect of planting beans 2 (Early) or 4 (Late) wk after planting corn on mixture dry matter yield over four environments. Means are separated with orthogonal contrasts within each bean planting date and mixture. Pairs of bars with * are different at P < 0.05.
The corn component of mixture yields was reduced by bean inclusion in all mixtures (Table 2). Corn yield reductions ranged from 5.8% in mixtures with scarlet runner bean to 11.5% in velvet bean mixtures. The corn forage yields in the low and normal density treatments were 15.9 and 19.9 Mg ha−1, respectively (Table 2). Cusicanqui and Lauer (1999) reported in Wisconsin that depending on location, as corn plant density increased from 44,500 to 104,500 plants ha−1, corn forage yield increased by 1.7 to 4.1 Mg ha−1
Corn forage yields were affected by the interaction of bean planting date and bean species (Table 2). The velvet bean mixture yielded 1.4 Mg ha−1 more (P = 0.0008) and the scarlet runner bean mixture yielded 1.0 Mg ha−1 more (P = 0.0168) in the late planting date compared with the early planting date, whereas corn yield was not affected by bean planting date in mixtures with lablab bean (Fig. 2
). The interaction of bean planting date and bean species occurred because early-planted scarlet runner bean grew rapidly in the early season and likely competed with corn for nutrients and light. Scarlet runner bean planted later did not have such a profound effect on corn forage yield. In 2005, early-planted velvet bean was highly productive, and the large leaves shaded corn plants from the time of silking through harvest. In Zimbabwe, Maasdorp and Titterton (1997) reported that a corn–velvet bean mixture had a bean concentration of 294 g kg−1 and a corn biomass yield of 4.1 Mg ha−1, which was only 50% of the corn DM yield without bean inclusion. Bryan and Materu (1987) reported that corn DM yield was reduced by 5 and 26% when corn was intercropped with cowpeas and climbing P. vulgaris, respectively.
Figure 2.
Effect of planting beans 2 (Early) or 4 (Late) wk after planting corn on corn dry matter yield over four environments. Means are separated with orthogonal contrasts within each bean planting date and mixture. Pairs of bars with * are different at P < 0.05.
Bean concentrations in mixtures were affected by bean planting date (Table 2). The early bean planting date treatment increased overall bean concentration from 62 to 73 g kg−1 DM (Table 2). An interaction of corn density and bean species for proportion of bean in mixtures was observed (Table 2). Corn density had no effect on the proportion of scarlet runner bean in mixtures; however, the lablab bean mixture contained 45 g kg−1 DM more (P < 0.0001) and the velvet bean mixture contained 48 g kg−1 DM more (P < 0.0001) in the low corn density treatment compared with the normal corn density treatment (Fig. 3
). The greater proportion and total biomass of lablab bean and velvet bean in mixture with low density corn suggest that both beans take advantage of more light and less competition with reduced corn plant density. Low scarlet runner bean proportions in mixtures were caused by a combination of potato leafhopper and white mold damage in both years. Bryan and Materu (1987) found that a corn density of 64 600 plants ha−1 and a P. vulgaris density of 215,200 plants ha−1 led to a bean concentration of 170 g kg−1 DM. In Zimbabwe, Maasdorp and Titterton (1997) did not find substantial bean concentrations by sowing beans in the same row as the corn. For most legumes tested (soybean, sunhemp [Crotalaria juncea L.], lablab bean, scarlet runner bean, cowpea), the proportion of legume was under 16% of total mixture biomass (Maasdorp and Titterton, 1997), and only the mixture with velvet bean approached 30%.
Figure 3.
Effect of planting corn at a density of 82,500 plants ha−1 (normal) or a density of 55,000 plants ha−1 (low) on bean concentration in mixtures over four environments. Means are separated with orthogonal contrasts within each corn density and mixture. Pairs of bars with * are different at P < 0.05.
Crude protein concentration of pure fractions of lablab bean (130 g kg−1 DM), scarlet runner bean (150 g kg−1 DM), and velvet bean (140 g kg−1 DM) were all found to be significantly higher than CP concentration in monoculture corn (61 g kg−1 DM) (data not shown). Crude protein concentration was also greater in the lablab (69 g kg−1 DM) and velvet bean (71 g kg−1 DM) mixtures compared with monoculture corn (61 g kg−1 DM) (Table 2). Because of white mold and leafhopper damage, scarlet runner bean did not contribute enough bean forage to the mixtures to increase CP concentration over that of monoculture corn. The low corn density treatment contained more CP (7 g kg−1 DM) than the normal corn density treatment because of the greater proportion of bean in these mixtures (Table 2). The Pearson correlation coefficient for bean and CP concentration was r = 0.54 (P < 0.0001), suggesting a moderately positive linear relationship between the two variables (data not shown).
Kaiser and Lesch (1977) found that lablab bean increased CP concentration by 44% as corn density decreased from 72,000 to 18,000 plants ha−1, with a constant lablab bean density of 108,000 plants ha−1 Bryan and Materu (1987) reported that intercropping cowpeas and corn increased CP concentration by 9% and produced similar yields to monoculture corn.
Pure fractions of lablab bean (400 g kg−1 DM) and scarlet runner bean (440 g kg−1 DM) were found to be significantly higher in NDF concentration compared with monoculture corn (360 g kg−1 DM), while velvet bean (370 g kg−1 DM) was not different from corn (data not shown). Neutral detergent fiber concentrations tended to be greater in mixtures with greater bean proportions (Table 2). The NDF concentration in mixtures was affected by corn density and bean species (Table 2). The low corn density treatment contained more NDF (6 g kg−1 DM) than the normal corn density treatment. Lablab bean and velvet bean mixtures contained greater NDF concentrations than monoculture corn. Lablab bean and velvet bean mixtures had the highest proportion of bean, which increased NDF concentrations in the early bean planting treatment. The Pearson correlation coefficient for bean and NDF concentration was r = 0.34 (P < 0.0001), suggesting a moderately positive linear relationship between the two variables (data not shown).
Pure fractions of lablab bean (790 g kg−1 DM), scarlet runner bean (760 g kg−1 DM), and velvet bean (810 g kg−1 DM) were all found to be significantly lower in IVTD concentration compared with monoculture corn (830 g kg−1 DM) (data not shown). In vitro true digestibility of monoculture corn and mixtures was high, varied little among treatments, but decreased slightly as proportions of bean in mixtures increased (Table 2). The IVTD concentration was affected by bean species; only the lablab bean (827 g kg−1 DM) and velvet bean (818 g kg−1 DM) mixtures were lower than monoculture corn (833 g kg−1 DM). Since these mixtures also contained the highest bean concentrations, IVTD concentrations were reduced accordingly. The Pearson correlation coefficient for bean and IVTD concentration was r = −0.30 (P < 0.0001), suggesting a moderately negative linear relationship between the two variables (data not shown).
Neutral detergent fiber digestibility, which ranged from 533 to 551 g kg−1 NDF, was affected little by bean or management treatments (Table 2). The velvet bean–corn mixture had lower NDFd than other mixtures or monoculture corn (Table 2). Generally, NDFd is greater for grasses than for legumes (National Research Council, 2001), but the magnitude of this difference and the proportions of legume in these mixtures were not great enough to have a significant effect on the mixtures.
Starch concentration, primarily driven by the amount of corn grain in mixtures, was affected by bean planting date, corn density, and bean species (Table 2). The late bean planting date contained 7 g kg−1 DM more starch than the early bean planting date. The normal corn density treatment contained 12 g kg−1 DM more starch than the low corn density treatment. Starch concentrations were greatest in monoculture corn and the scarlet runner mixture, both of which contained a lower proportion of bean compared with the other two mixtures (Table 2).
The data suggest that as bean concentration in mixtures is increased, starch concentrations decline. Starch concentration in vegetative legume forage is much lower than in corn, often ranging from 2 to 48 g kg−1 DM in fresh alfalfa depending on cutting time (Owens et al., 1999). The lower starch concentration in corn–bean mixtures could also be associated with grain yield loss caused by competition with the beans for light and other resources (Ngouajio et al., 1999). The Pearson correlation coefficient for bean and starch concentration was r = −0.53 (P < 0.0001), suggesting a moderately negative linear relationship between the two variables (data not shown).
Calculated milk per megagram forage is an estimate of milk production that can be attributed to that forage fed in a total mixed ration, calculated from the MILK2000 spreadsheet (Schwab et al., 2003). Calculated milk per megagram forage was affected by bean species (Table 2). The velvet bean mixture was significantly lower than the other mixtures, with a calculated milk per megagram forage of 1740 kg Mg−1 (Table 2). The scarlet runner bean mixture, although not different from monoculture corn, produced the highest milk per megagram forage at 1810 kg Mg−1 (Table 2). Cox and Cherney (2005) reported 1692 kg milk Mg−1 forage, averaged over three corn hybrids in New York. The corn hybrids reported by Cox and Cherney (2005) had higher NDF and lower starch values compared with the current research, resulting in lower calculated milk production.
Calculated milk per hectare integrates forage nutritive value and yield through the MILK2000 spreadsheet (Schwab et al., 2003). Calculated milk per hectare was affected by bean planting date, corn density, and bean species (Table 2). The late bean planting date treatment produced 1000 kg ha−1 more calculated milk per hectare compared with the early bean planting date treatment (Table 2). The normal corn density treatment produced 6500 kg ha−1 more calculated milk per hectare compared with the low corn density treatment (Table 2). The velvet bean mixture was lower in calculated milk per hectare (32,600 kg ha−1) compared with all other mixtures or monoculture corn (Table 2). The lablab bean mixture, although not significantly different from monoculture corn or the scarlet runner bean mixture, had the highest calculated milk per hectare, at 35,000 kg ha−1 (Table 2).
The normal corn density treatment produced more calculated milk per hectare because more forage was harvested compared with the low corn density treatment plots. In addition, the early bean planting date treatment had a higher bean percentage and thus a reduced corn yield and subsequently lower calculated milk per hectare. Velvet bean reduced mixture forage yield in 2005 and subsequently reduced calculated milk per hectare. Because calculated milk per hectare is a variable related to forage yield and nutritive value, any reduction in either reduces the values of calculated milk per hectare. Cox and Cherney (2005) reported 25,700 kg calculated milk ha−1 averaged over three corn hybrids. Average DM yields for the same hybrids was 14.9 Mg ha−1 (Cox and Cherney, 2005), approximately 25% lower than in the current research.
The lablab bean mixture had slightly higher yield (not significant at P < 0.05) but also a slightly higher NDF concentration than monoculture corn. The yield- and energy-based MILK2000 model reveals no advantage to growing lablab bean with corn for forage. Because the model does not take into account the added value of additional CP in the ration, FEEDVAL4, a model that considers the value of feed ingredients (Howard and Shaver, 1997) was used to evaluate corn–bean mixtures.
The feed nutrient value was greater in the corn–bean mixtures compared with monoculture corn. Only the lablab bean mixture, however, increased crop value over monoculture corn (Table 3
). The feed nutrient value, based on value of feed ingredients, was greatest for the lablab bean and velvet bean mixtures ($80 Mg−1 DM) (Table 3). These values were greater than the other mixtures primarily because of a higher CP concentration in the two mixtures. When the feed nutrient value is multiplied by the total DM yield on a land basis, an estimated value can be given to a crop. The crop value of the mixtures per hectare was the greatest for the lablab bean mixture ($1570 ha−1) (Table 3) because of high feed nutrient value and high yield. While the velvet bean mixture had a high feed nutrient value (dollar per megagram DM), total mixture yield was relatively low and thus it was not worth as much per hectare. FEEDVAL4 estimates the value of feeds based on the price of different nutrients with CP being a major contributor. So, both feed nutrient value (dollar per megagram DM) and crop value (dollar per hectare) will change with market price for feed ingredients. The lablab bean mixture was the most promising because of additional value associated with higher CP concentration along with no reduction in total DM yield compared with monoculture corn.
View Full Table | Close Full ViewTable 3.
Calculated forage nutrient value of corn–bean mixtures pooled over four environments.
Mixtures
Feed nutrient value
Crop value
$ Mg−1 DM
$ ha−1
Corn
77 c
1480 b
Lablab bean mix
80 a
1570 a
Scarlet runner bean mix
79 b
1490 b
Velvet bean mix
80 a
1500 b
†
Within columns, values are averaged across treatment levels.
‡
Within columns, means followed by different letters are significantly different at P < 0.05.
CONCLUSIONS
While CP concentration was increased in lablab bean– and velvet bean–corn mixtures, fiber and digestibility were compromised compared with monoculture corn. Increased bean concentration in the velvet bean mixture negatively affected calculated milk production per megagram forage (70 kg Mg−1 less) and milk per hectare (2100 kg ha−1 less). In contrast, the lablab bean mixture was not different from monoculture corn in terms of calculated milk per hectare. Lablab bean proved to have the best potential as an intercrop with corn in these experiments. Yield of the lablab bean mixture was not different from monoculture corn and had a calculated feed nutrient value higher than monoculture corn. These experiments show that lablab bean grown with corn has the greatest potential of the three beans to increase CP concentration above monoculture corn without compromising forage yield or calculated milk hectare and potentially increasing crop value (dollar per hectare) in the northern United States. On the basis of this study, the additional costs connected with bean seeds, machinery, and labor costs associated with a separate operation must be less than $90 ha−1 for the lablab bean mixture to be a profitable alternative to monoculture corn.
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5585 Guilford Road | Madison, WI 53711-5801 | 608-273-8080 | Fax 608-273-2021
Certification: 608-273-8085 | Fax 608-273-2081
1. Kevin L. Armstrong *a,
2. Kenneth A. Albrechtb,
3. Joseph G. Lauerb and
4. Heathcliffe Ridayc
+ Author Affiliations
1.
a Dep. of Crop Sciences, Univ. of Illinois, 1102 S. Goodwin Ave., Urbana, IL 61801
b 1575 Linden Dr., Dep. of Agronomy, Univ. of Wisconsin-Madison, Madison, WI 53706
c U.S. Dairy Forage Research Center, USDA-ARS, 1925 Linden Dr., Madison, WI 53706. This research was partially supported by funding through USDA Cooperative State Research Education and Extension Service (CSREES) Hatch Project WIS04802
Abstract
This experiment was designed to determine if intercropping corn (Zea mays L.) with climbing beans is a viable option to increase crude protein (CP) concentration in forage rather than purchasing costly protein supplements for livestock rations. In these experiments, corn was intercropped with three beans—lablab bean [Lablab purpureus (L.) Sweet], velvet bean [Mucuna pruriens (L.) D.C.], and scarlet runner bean (Phaseolus coccineus L.)—or grown in monoculture near Arlington and Lancaster, WI. Corn was sown in early May and late April in 2004 and 2005, respectively, and later thinned to 55,000 (low density) or 82,500 (normal density) plants ha−1 Beans were sown in rows 8 cm on one side of the corn rows at 82,500 plants ha−1 2 or 4 wk after corn planting. Averaged over four environments, mixture forage dry matter (DM) yields were similar. However the velvet bean and scarlet runner bean mixtures produced significantly higher forage DM yield, 1.2 Mg ha−1 and 0.89 Mg ha−1 more, respectively, in the late bean planting treatment. Beans, except scarlet runner bean, which was damaged by mold and insects, increased the CP concentration of all mixtures, with the greatest increases from the lablab bean (13%) and velvet bean (16%). The experiments show that lablab bean grown with corn has the greatest potential of the three beans to increase CP concentration above monoculture corn, without compromising forage yield or calculated milk ha−1 and increasing forage nutrient value.
Abbreviations
CP, crude protein;
DIP, degradable intake protein;
DM, dry matter;
IVTD, in vitro true digestibility;
NDF, neutral detergent fiber;
NDFd,;
NIRS, near infrared reflectance spectroscopy;
SECV, standard error of cross validation;
UIP, undegradable intake protein
Corn (Zea mays
L.) silage is a high-producing forage crop in the United States, with 2.4 million ha planted and 96.4 million Mg harvested in 2005 (USDA-NASS, 2006). Wisconsin was the top corn silage–producing state in the United States, with 13.6 million Mg harvested from 356,000 ha in 2005 (USDA-NASS, 2006). Corn silage is an important source of forage for dairy cattle in the United States because of its relatively consistent nutritive value, high yield, and high energy density compared with other forage crops (Coors and Lauer, 2001). One limitation to corn silage as a feed for dairy cows is low crude protein (CP) concentration; Darby and Lauer (2002) reported an average whole corn plant forage CP concentration of 73 g kg−1 across Wisconsin.
Because supplementing dairy rations with protein concentrate can be costly, alternatives have been explored. For example, Herbert et al. (1984) found intercropping corn and soybean [Glycine max (L.) Merr.] in alternating rows to be beneficial, with a 19 to 36% increase in CP concentration over monoculture corn with similar forage yields. Climbing beans have also been grown, with varying degrees of success, as an intercrop with corn. Kaiser and Lesch (1977) found that lablab bean [Lablab purpureus (L.) Sweet] increased CP concentration by 44% and lowered forage dry matter (DM) yield by 28%, with a lablab bean density of 108,000 plants ha−1 and a corn density ranging from 72,000 to 18,000 plants ha−1 They reported up to a 78% increase in CP concentration from monoculture corn at a density of 54,000 plants ha−1 to an intercrop density of corn at 18,000 plants ha−1 and a constant lablab bean density of 108,000 plants ha−1 Bryan and Materu (1987) found that intercropping cowpeas (Vigna unguiculata L. Walp) and corn increased CP concentration by 9% and did not lower forage DM yield compared with monoculture corn.
In Ghana, Haizel (1974) reported no difference in forage DM yield, harvested at full grain maturity, of monoculture corn or corn intercropped with cowpea in a good growing season. In Zimbabwe, Maasdorp and Titterton (1997) found varying results when intercropping climbing beans and corn. While lablab bean and scarlet runner bean (Phaseolus coccineus L.) did not produce much bean biomass in mixtures (less than 16%), velvet bean [Mucuna pruriens (L.) D.C.] accounted for nearly 30% of the intercrop mixture and depressed corn yield by 50%. The literature is inconclusive on the viability of such intercropping systems, and little information is available for production in cooler temperate regions.
This experiment was designed to determine if intercropping corn with beans is a viable option to increase CP concentration in forage rather than purchasing costly CP supplements for dairy cattle rations in a northern environment. The objectives of this research were to compare monoculture corn with corn–bean mixtures in terms of DM yield, nutritive value, potential milk production, and forage nutrient value and determine how these factors are affected by timing of bean planting and changes in corn density.
MATERIALS AND METHODS
Field experiments were conducted in 2004 and 2005 at the University of Wisconsin Agricultural Research Stations near Arlington (43°18′ N, 89°21′ W) and Lancaster (42°50′ N, 90°47′ W), WI. The experiments at Arlington were conducted on Plano silt loam (fine-silty, mixed, mesic Typic Argiudolls), on relatively flat and well-drained fields and at Lancaster on Rozetta silt loam (fine-silty, mixed, superactive, mesic Typic Hapludalfs), on a flat and well-drained field. The previous crop at Arlington was soybean both years. The previous crops at Lancaster were oat (Avena sativa L.) in 2004 and beans intercropped with corn in 2005. Before tillage, 170 and 135 kg ha−1 N as urea were applied at Arlington in 2004 and 2005, respectively. Before tillage, 180 kg ha−1 N as urea was applied at Lancaster in both years. Tillage operations included chisel plowing and field cultivation at Arlington and chisel plowing followed by a soil finisher at Lancaster in both years. Soil fertility levels at both locations were maintained at optimal levels for corn silage production (Kelling et al., 1998). Glyphosate [N-(phosphonomethyl)glycine] resistant corn hybrid DKC 50-20 was planted on 4 May 2004 and 25 Apr. 2005 at Arlington and 3 May 2004 and 26 Apr. 2005 at Lancaster. Corn was sown with 76.2-cm row spacing at 83,750 and 93,000 seeds ha−1 at both locations in 2004 and 2005, respectively, and later hand thinned to designated corn densities. Permethrin [(3- phenoxyphenyl) methyl(±)cis-trans 3-(2,2-dichloroethenyl)-2,2-dimethylcyclopropanecarboxylate] (6 g ha−1 a.i.), for control of corn wireworm [Melanotus communis (Gyll.)] and seed corn maggot [Delia platura (Meigen)], and carboxin (5,6-dihydro-2-methyl-N-phenyl-1,4-oxathin-3-carboxamide) (8 g ha−1 a.i.), for control of seed rots and decay, were applied at planting at Lancaster in both years. Glyphosate was spot sprayed for weed control at both locations, avoiding herbicide contact with the bean, along with hand weeding. Flumetsulam [N-(2,6-difluorophenyl)-5-methyl(1,2,4)triazolo (1,5-a)pyrimidine-2-sulfonamide] (28 g ha−1 a.i.) and s-metolachlor + safener [(1S)-2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide] (1.42 kg ha−1 a.i.) were applied pre-emergence to control weeds at both locations in 2005.
Experimental treatments included corn density, bean planting date, and species. The three bean species used were ‘Rongai’ lablab bean, velvet bean (speckled germplasm, from Sharad Phatak, University of Georgia), and ‘Scarlet Emperor’ scarlet runner bean. On the basis of a review of the literature, these bean species appear to have the best potential as intercrops with corn. Bean seeds were inoculated with appropriate rhizobia (Nitragin, Inc., Milwaukee, WI) and hand planted about 8 cm to one side of the corn rows at 2 or 4 wk after corn sowing. It was hypothesized that a delayed planting of beans alongside corn may be necessary to avoid excessive bean competition with corn development as noted by Maasdorp and Titterton (1997) When corn plants reached the V6 stage (Ritchie et al., 1992), they were hand thinned to a low density (55,000 plants ha−1) and a normal density (82,500 plants ha−1). Bean density was kept constant at 82,500 plants ha−1 It was hypothesized that a lower corn density would allow greater bean development and subsequently greater CP in harvested forage.
The experimental design was a randomized complete block design in 2004 and 2005 with four replications at each location. Treatments were the factorial combination of two bean sowing dates, two corn densities, and three bean species. There was a corn control. Experimental units consisted of three corn rows in 2004 and four corn rows in 2005 with associated bean rows in 8.2-m long plots. The middle corn row in 2004 and one of the middle rows in each experimental unit in 2005 were harvested for forage.
On the day of harvest, representative corn and bean plants were removed from selected harvest rows with treatments of normal corn density and early bean planting date for near infrared reflectance spectroscopy (NIRS) equation development to estimate bean and corn proportions in the mixtures. Separated corn and bean plants were dried at 60°C, weighed, and ground with a Christy hammer mill (Christy, Suffolk, UK) equipped with a 1-mm screen. Pure fractions (48 per experiment per year) and mixtures (36 per experiment per year) of corn and bean created by combining the pure fractions were used for NIRS equation development. Samples were scanned with a NIRSystems 6500 near infrared reflectance spectrophotometer (FOSS NIRSystems Inc., Eden Prairie, MN) equipped with a ring cup autosampler. Near-infrared reflectance spectra (1/R) were obtained between the wavelengths of 400 and 2498 nm. Data management and equation development were performed using WinISI 1.50 (Infrasoft International, Port Matilda, PA). Calibration statistics, [coefficient of determination [R
2] and standard error of cross validation [SECV]), for determining bean concentration in validation mixtures were SECV = 37 (R
2 = 0.99) and SECV = 20 (R
2 = 0.99) for separate equations developed in 2004 and 2005, respectively (Martens and Naes, 1989; Shenk and Westerhaus, 1991, 1994). Equations were used to predict bean concentration in mixtures and corn concentration was determined by subtracting bean concentration from total plot DM mass.
The middle row of each plot was harvested at the 50% kernel milk line stage (Afuakwa and Crookston, 1984) on 17 Sept. 2004 and 7 Sept. 2005 at Lancaster and 20 Sept. 2004 and 15 Sept. 2005 at Arlington. Harvest rows were chopped to a theoretical cutting length of 1 cm with a small, commercial forage harvester, and a 1-kg subsample was collected and dried at 60°C to determine DM concentration of the forage. The dried subsample was ground with a hammer mill equipped with a 1-mm screen.
Forages were analyzed for total N concentration by the Dumas method (AOAC, 1990) with an automated analyzer (LECO Model FP-528; LECO Corp., St. Joseph, MI). Crude protein concentrations were calculated by multiplying total N by 6.25. Neutral detergent fiber (NDF) concentrations were determined by the batch procedure outlined by ANKOM Technology Corp. (Fairport, NY). Subsamples (0.25 g each) were analyzed for in vitro true digestibility (IVTD) using rumen fluid from a lactating Holstein cow on a total mixed ration and buffer solution described by Goering and Van Soest (1970) with the Daisy II200 in vitro incubator and the ANKOM200 fiber analyzer (ANKOM Technology Corp., Fairport, NY). Neutral detergent fiber digestibility (NDFd) was calculated from the NDF and IVTD values as 100{[NDF − (100 − IVTD)]/NDF}. Ash concentration was determined by combustion of a 1.0-g subsample at 600°C for 2 h (data not shown but used in milk production models). Starch concentrations were determined by the procedures of Rong et al. (1996) and Owens et al. (1999)
Potential milk production estimates were calculated according to MILK2000 (Schwab et al., 2003). Milk per megagram DM and milk per hectare were calculated for corn and mixture forages. Values for ether extract were estimated from weighted values of corn silage (National Research Council, 2001) and lablab bean (Díaz et al., 2003) (depending on bean percentage in mixtures), while neutral detergent insoluble CP values were estimated from weighted values of corn silage and alfalfa (Medicago sativa L.) in the NRC tables (National Research Council, 2001).
Forage nutrient values were calculated with FEEDVAL4, a spreadsheet developed to assign a dollar value to feed ingredients (Howard and Shaver, 1997). The term forage nutrient value refers to the output of the spreadsheet, which allows the user to compare feeds based on current prices of feed ingredients and determine cost effectiveness. The FEEDVAL4 spreadsheet uses blood meal (undegradable intake protein, UIP), urea (degradable intake protein, DIP), shelled corn (energy), tallow (fat), dicalcium phosphate (phosphorus), and calcium carbonate (calcium) as reference feed ingredients. Prices for reference ingredients were based on April 2006 market values. The DM and CP components were measured values, while the total digestible nutrients of the mixtures were calculated using MILK2000. The UIP and DIP percentages of CP were estimated from a combination of corn silage and alfalfa values according to Linn et al. (1994), while the fat concentration was estimated from corn silage (National Research Council, 2001). Calcium and phosphorus concentrations for corn and beans were estimated from corn silage and alfalfa values from the NRC tables (National Research Council, 2001). Feed nutrient values were determined for 1 Mg of DM of each mixture and then multiplied by the corresponding mixture yield to provide crop value for 1 ha of each mixture.
Data from both years were pooled and analyzed as a randomized complete block design with the Windows version of SAS software package release 9.1 (SAS Institute, 2002). Tests concerning heterogeneity of variances were conducted to assess the appropriateness of pooling the data; however, no such problems existed in subsequent models. The MIXED procedure (SAS Institute, 2002) was used to detect treatment differences for the response variables of mixture yield, forage composition, milk per megagram and per hectare, and forage nutrient values. Corn density, bean planting date, and bean species were considered fixed effects, while years, locations, and blocks nested within locations were considered random effects. The Type 3 test of fixed effects was used to gauge significance at P < 0.05, and significant main effects were explored using the LSMEANS statement of SAS (SAS Institute, 2002). Fisher's protected LSD (P = 0.05) with no adjustment was used to compare mixture means, using the PDMIX800 macro (Saxton, 1998) when appropriate. Orthogonal contrast sets were also used to explore differences among significant (P < 0.05) interactions. The Pearson correlation coefficient, calculated in the CORR procedure (SAS Institute, 2002), was used to detect correlations between bean concentration in mixtures and mixture CP, NDF, IVTD, and starch concentrations.
RESULTS AND DISCUSSION
Environment and Crop Development
May 2004 was unusually cool and wet, delaying emergence and early seedling development of both corn and beans. Above-average precipitation occurred in May 2004 at both Arlington and Lancaster (Table 1
). The weather in May 2005 provided drier environments at both locations. In 2005, Arlington received less-than-normal precipitation in June and early July, so the site was irrigated with 51 mm of water on 28 June, 11 July, and 19 July to supplement rainfall to near-normal levels.
View Full Table | Close Full ViewTable 1.
Mean monthly precipitation and temperature at the Arlington and Lancaster Research Stations, WI, in 2004 and 2005 growing seasons.
Arlington
Lancaster
Month
Normal
2004
2005
Normal
2004
2005
Total monthly precipitation
mm
Apr.
82
48
20
85
37
54
May
87
261
85
92
280
68
June
103
104
88
120
123
169
July
98
110
214
107
101
140
Aug.
108
72
78
117
92
106
Sept.
93
13
119
81
2
76
Oct.
62
83
15
61
69
12
Total
633
691
619
663
704
625
Average monthly temperature
°C
Apr.
8
9
10
8
9
11
May
15
13
12
15
14
13
June
20
18
22
20
19
22
July
22
21
23
22
20
22
Aug.
21
18
21
21
18
21
Sept.
16
18
19
16
18
19
Oct.
10
10
10
10
10
11
Avg.
16
15
17
16
15
17
†
Normal precipitation and temperatures are based on 30-yr means.
‡
Precipitation includes 51 and 102 mm of irrigation in June and July.
Early bean growth in both years was variable among the different bean species. Scarlet runner bean emerged first in the early bean planting date and quickly grew to the height of the corn plants at stage V3 (Ritchie et al., 1992) by early June. In 2004, velvet bean seedlings emerged later and took several weeks to reach a full stand due to cool, wet soil, similar to the observations of Tracy and Coe (1918) Lablab bean emergence was intermediate, but seedlings were not as vigorous as scarlet runner bean. In 2005, velvet bean and lablab bean seedlings developed similarly in the drier spring. Once the weather was warmer, velvet bean began to grow better; however, much of its biomass was attributed to stems rather than leaves. Both scarlet runner bean and velvet bean produced some pods, but day length was too long for flower production in lablab bean. Velvet bean produced more biomass than did lablab bean in 2005 (data not shown); in addition, corn lodged due to the weight of the velvet bean biomass before harvest in the velvet bean plots. The difference in velvet bean performance between years was associated with cool and wet spring conditions in 2004 compared with 2005 (Tracy and Coe, 1918).
Forage Yield and Nutritive Value
Mixture forage DM yields were affected by corn planting density and were 17.2 and 21.0 Mg ha−1 for low and normal corn densities, respectively (Table 2
). The reduction in yield was associated with a 27,500 plants ha−1 difference between normal and low corn density treatments for which addition of beans did not compensate.
View Full Table | Close Full ViewTable 2.
Model significance levels, forage yield and nutritive value, and bean planting date and corn density effects of corn and bean mixtures pooled over four environments.
Total yield
Corn yield
Bean in mix
CP
NDF
IVTD
Starch
NDFd
Milk per megagram
Milk per hectare
Source of variation
P > F
Date
0.0023
0.0006
0.0389
NS
NS
NS
0.0333
NS
NS
0.0148
Density
<0.0001
<0.0001
<0.0001
<0.0001
0.0476
NS
0.0008
NS
NS
<0.0001
Date × density
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Species
0.0031
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0003
<0.0001
0.0002
Date × species
0.0346
0.0392
NS
NS
NS
NS
NS
NS
NS
NS
Density × species
NS
NS
0.0027
NS
NS
NS
NS
NS
NS
NS
Date × density × species
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
Mixture effects
Species
Mg ha−1
g kg−1 DM
g kg−1 NDF
kg Mg−1
kg ha−1
Corn
19.1 ab
19.1 a
0 c
61 c
372 b
833 a
388 a
551 a
1810 a
34700 a
Lablab bean mix
19.6 a
17.5 bc
114 a
69 b
383 a
827 b
364 b
549 a
1780 b
35000 a
Scarlet runner bean mix
19.0 b
18.0 b
52 b
64 c
367 b
835 a
385 a
550 a
1810 a
34300 a
Velvet bean mix
18.7 b
16.9 c
103 a
71 a
389 a
818 c
360 b
533 b
1740 c
32600 b
Bean planting date and corn density effects
Bean planting date
Early
18.8 b
17.5 b
73 a
67
380
828
371 b
547
1790
33700 b
Late
19.4 a
18.2 a
62 b
66
375
829
378 a
544
1790
34700 a
Corn density
Normal
21.0 a
19.9 a
54 b
63 b
375 b
828
380 a
543
1780
37400 a
Low
17.2 b
15.9 b
81 a
70 a
381 a
828
368 b
548
1790
30900 b
†
CP, crude protein; NDF, neutral detergent fiber; IVTD, in vitro true digestibility; NDFd, neutral detergent fiber digestibility.
‡
NS, not significant.
§
Within columns, means followed by different letters are significantly different at P < 0.05.
Mixture forage DM yields were also affected by the interaction of bean planting date and bean species. Planting date had no effect on lablab bean–corn mixture yields; however, the velvet bean mixture yielded 1.2 Mg ha−1 more (P = 0.0011) and the scarlet runner bean mixture yielded 0.89 Mg ha−1 more (P = 0.0161) in the late planting date compared with the early planting date (Fig. 1
). In 2004, the early planted scarlet runner bean grew rapidly and became competitive with the corn plants early in the season but by harvest time contributed little because of potato leafhopper [Empoasca fabae (Harris)] and white mold [Sclerotinia sclerotiorum (Lib.) de Bary] damage. Lablab bean and velvet bean were not damaged by these pests. The later bean planting date did not have such a profound effect on yield because there was less early competition with corn. In 2005, the velvet bean mixture produced more with a late bean planting date because of greater bean biomass and corn lodging seen with the early bean planting date. In South Africa, Kaiser and Lesch (1977) reported a 7% DM yield reduction in a lablab bean–corn mixture compared with monoculture corn. Bryan and Materu (1987) did not find a significant difference between monoculture forage corn yield (8.8 Mg ha−1) and a corn–cowpea mixture yield (9.4 Mg ha−1) in West Virginia. Corn–bean mixture yields in the current Wisconsin experiments were approximately double those reported by Kaiser and Lesch (1977) and Bryan and Materu (1987), suggesting that environmental conditions were not optimal for corn production in the earlier experiments.
Figure 1.
Effect of planting beans 2 (Early) or 4 (Late) wk after planting corn on mixture dry matter yield over four environments. Means are separated with orthogonal contrasts within each bean planting date and mixture. Pairs of bars with * are different at P < 0.05.
The corn component of mixture yields was reduced by bean inclusion in all mixtures (Table 2). Corn yield reductions ranged from 5.8% in mixtures with scarlet runner bean to 11.5% in velvet bean mixtures. The corn forage yields in the low and normal density treatments were 15.9 and 19.9 Mg ha−1, respectively (Table 2). Cusicanqui and Lauer (1999) reported in Wisconsin that depending on location, as corn plant density increased from 44,500 to 104,500 plants ha−1, corn forage yield increased by 1.7 to 4.1 Mg ha−1
Corn forage yields were affected by the interaction of bean planting date and bean species (Table 2). The velvet bean mixture yielded 1.4 Mg ha−1 more (P = 0.0008) and the scarlet runner bean mixture yielded 1.0 Mg ha−1 more (P = 0.0168) in the late planting date compared with the early planting date, whereas corn yield was not affected by bean planting date in mixtures with lablab bean (Fig. 2
). The interaction of bean planting date and bean species occurred because early-planted scarlet runner bean grew rapidly in the early season and likely competed with corn for nutrients and light. Scarlet runner bean planted later did not have such a profound effect on corn forage yield. In 2005, early-planted velvet bean was highly productive, and the large leaves shaded corn plants from the time of silking through harvest. In Zimbabwe, Maasdorp and Titterton (1997) reported that a corn–velvet bean mixture had a bean concentration of 294 g kg−1 and a corn biomass yield of 4.1 Mg ha−1, which was only 50% of the corn DM yield without bean inclusion. Bryan and Materu (1987) reported that corn DM yield was reduced by 5 and 26% when corn was intercropped with cowpeas and climbing P. vulgaris, respectively.
Figure 2.
Effect of planting beans 2 (Early) or 4 (Late) wk after planting corn on corn dry matter yield over four environments. Means are separated with orthogonal contrasts within each bean planting date and mixture. Pairs of bars with * are different at P < 0.05.
Bean concentrations in mixtures were affected by bean planting date (Table 2). The early bean planting date treatment increased overall bean concentration from 62 to 73 g kg−1 DM (Table 2). An interaction of corn density and bean species for proportion of bean in mixtures was observed (Table 2). Corn density had no effect on the proportion of scarlet runner bean in mixtures; however, the lablab bean mixture contained 45 g kg−1 DM more (P < 0.0001) and the velvet bean mixture contained 48 g kg−1 DM more (P < 0.0001) in the low corn density treatment compared with the normal corn density treatment (Fig. 3
). The greater proportion and total biomass of lablab bean and velvet bean in mixture with low density corn suggest that both beans take advantage of more light and less competition with reduced corn plant density. Low scarlet runner bean proportions in mixtures were caused by a combination of potato leafhopper and white mold damage in both years. Bryan and Materu (1987) found that a corn density of 64 600 plants ha−1 and a P. vulgaris density of 215,200 plants ha−1 led to a bean concentration of 170 g kg−1 DM. In Zimbabwe, Maasdorp and Titterton (1997) did not find substantial bean concentrations by sowing beans in the same row as the corn. For most legumes tested (soybean, sunhemp [Crotalaria juncea L.], lablab bean, scarlet runner bean, cowpea), the proportion of legume was under 16% of total mixture biomass (Maasdorp and Titterton, 1997), and only the mixture with velvet bean approached 30%.
Figure 3.
Effect of planting corn at a density of 82,500 plants ha−1 (normal) or a density of 55,000 plants ha−1 (low) on bean concentration in mixtures over four environments. Means are separated with orthogonal contrasts within each corn density and mixture. Pairs of bars with * are different at P < 0.05.
Crude protein concentration of pure fractions of lablab bean (130 g kg−1 DM), scarlet runner bean (150 g kg−1 DM), and velvet bean (140 g kg−1 DM) were all found to be significantly higher than CP concentration in monoculture corn (61 g kg−1 DM) (data not shown). Crude protein concentration was also greater in the lablab (69 g kg−1 DM) and velvet bean (71 g kg−1 DM) mixtures compared with monoculture corn (61 g kg−1 DM) (Table 2). Because of white mold and leafhopper damage, scarlet runner bean did not contribute enough bean forage to the mixtures to increase CP concentration over that of monoculture corn. The low corn density treatment contained more CP (7 g kg−1 DM) than the normal corn density treatment because of the greater proportion of bean in these mixtures (Table 2). The Pearson correlation coefficient for bean and CP concentration was r = 0.54 (P < 0.0001), suggesting a moderately positive linear relationship between the two variables (data not shown).
Kaiser and Lesch (1977) found that lablab bean increased CP concentration by 44% as corn density decreased from 72,000 to 18,000 plants ha−1, with a constant lablab bean density of 108,000 plants ha−1 Bryan and Materu (1987) reported that intercropping cowpeas and corn increased CP concentration by 9% and produced similar yields to monoculture corn.
Pure fractions of lablab bean (400 g kg−1 DM) and scarlet runner bean (440 g kg−1 DM) were found to be significantly higher in NDF concentration compared with monoculture corn (360 g kg−1 DM), while velvet bean (370 g kg−1 DM) was not different from corn (data not shown). Neutral detergent fiber concentrations tended to be greater in mixtures with greater bean proportions (Table 2). The NDF concentration in mixtures was affected by corn density and bean species (Table 2). The low corn density treatment contained more NDF (6 g kg−1 DM) than the normal corn density treatment. Lablab bean and velvet bean mixtures contained greater NDF concentrations than monoculture corn. Lablab bean and velvet bean mixtures had the highest proportion of bean, which increased NDF concentrations in the early bean planting treatment. The Pearson correlation coefficient for bean and NDF concentration was r = 0.34 (P < 0.0001), suggesting a moderately positive linear relationship between the two variables (data not shown).
Pure fractions of lablab bean (790 g kg−1 DM), scarlet runner bean (760 g kg−1 DM), and velvet bean (810 g kg−1 DM) were all found to be significantly lower in IVTD concentration compared with monoculture corn (830 g kg−1 DM) (data not shown). In vitro true digestibility of monoculture corn and mixtures was high, varied little among treatments, but decreased slightly as proportions of bean in mixtures increased (Table 2). The IVTD concentration was affected by bean species; only the lablab bean (827 g kg−1 DM) and velvet bean (818 g kg−1 DM) mixtures were lower than monoculture corn (833 g kg−1 DM). Since these mixtures also contained the highest bean concentrations, IVTD concentrations were reduced accordingly. The Pearson correlation coefficient for bean and IVTD concentration was r = −0.30 (P < 0.0001), suggesting a moderately negative linear relationship between the two variables (data not shown).
Neutral detergent fiber digestibility, which ranged from 533 to 551 g kg−1 NDF, was affected little by bean or management treatments (Table 2). The velvet bean–corn mixture had lower NDFd than other mixtures or monoculture corn (Table 2). Generally, NDFd is greater for grasses than for legumes (National Research Council, 2001), but the magnitude of this difference and the proportions of legume in these mixtures were not great enough to have a significant effect on the mixtures.
Starch concentration, primarily driven by the amount of corn grain in mixtures, was affected by bean planting date, corn density, and bean species (Table 2). The late bean planting date contained 7 g kg−1 DM more starch than the early bean planting date. The normal corn density treatment contained 12 g kg−1 DM more starch than the low corn density treatment. Starch concentrations were greatest in monoculture corn and the scarlet runner mixture, both of which contained a lower proportion of bean compared with the other two mixtures (Table 2).
The data suggest that as bean concentration in mixtures is increased, starch concentrations decline. Starch concentration in vegetative legume forage is much lower than in corn, often ranging from 2 to 48 g kg−1 DM in fresh alfalfa depending on cutting time (Owens et al., 1999). The lower starch concentration in corn–bean mixtures could also be associated with grain yield loss caused by competition with the beans for light and other resources (Ngouajio et al., 1999). The Pearson correlation coefficient for bean and starch concentration was r = −0.53 (P < 0.0001), suggesting a moderately negative linear relationship between the two variables (data not shown).
Calculated milk per megagram forage is an estimate of milk production that can be attributed to that forage fed in a total mixed ration, calculated from the MILK2000 spreadsheet (Schwab et al., 2003). Calculated milk per megagram forage was affected by bean species (Table 2). The velvet bean mixture was significantly lower than the other mixtures, with a calculated milk per megagram forage of 1740 kg Mg−1 (Table 2). The scarlet runner bean mixture, although not different from monoculture corn, produced the highest milk per megagram forage at 1810 kg Mg−1 (Table 2). Cox and Cherney (2005) reported 1692 kg milk Mg−1 forage, averaged over three corn hybrids in New York. The corn hybrids reported by Cox and Cherney (2005) had higher NDF and lower starch values compared with the current research, resulting in lower calculated milk production.
Calculated milk per hectare integrates forage nutritive value and yield through the MILK2000 spreadsheet (Schwab et al., 2003). Calculated milk per hectare was affected by bean planting date, corn density, and bean species (Table 2). The late bean planting date treatment produced 1000 kg ha−1 more calculated milk per hectare compared with the early bean planting date treatment (Table 2). The normal corn density treatment produced 6500 kg ha−1 more calculated milk per hectare compared with the low corn density treatment (Table 2). The velvet bean mixture was lower in calculated milk per hectare (32,600 kg ha−1) compared with all other mixtures or monoculture corn (Table 2). The lablab bean mixture, although not significantly different from monoculture corn or the scarlet runner bean mixture, had the highest calculated milk per hectare, at 35,000 kg ha−1 (Table 2).
The normal corn density treatment produced more calculated milk per hectare because more forage was harvested compared with the low corn density treatment plots. In addition, the early bean planting date treatment had a higher bean percentage and thus a reduced corn yield and subsequently lower calculated milk per hectare. Velvet bean reduced mixture forage yield in 2005 and subsequently reduced calculated milk per hectare. Because calculated milk per hectare is a variable related to forage yield and nutritive value, any reduction in either reduces the values of calculated milk per hectare. Cox and Cherney (2005) reported 25,700 kg calculated milk ha−1 averaged over three corn hybrids. Average DM yields for the same hybrids was 14.9 Mg ha−1 (Cox and Cherney, 2005), approximately 25% lower than in the current research.
The lablab bean mixture had slightly higher yield (not significant at P < 0.05) but also a slightly higher NDF concentration than monoculture corn. The yield- and energy-based MILK2000 model reveals no advantage to growing lablab bean with corn for forage. Because the model does not take into account the added value of additional CP in the ration, FEEDVAL4, a model that considers the value of feed ingredients (Howard and Shaver, 1997) was used to evaluate corn–bean mixtures.
The feed nutrient value was greater in the corn–bean mixtures compared with monoculture corn. Only the lablab bean mixture, however, increased crop value over monoculture corn (Table 3
). The feed nutrient value, based on value of feed ingredients, was greatest for the lablab bean and velvet bean mixtures ($80 Mg−1 DM) (Table 3). These values were greater than the other mixtures primarily because of a higher CP concentration in the two mixtures. When the feed nutrient value is multiplied by the total DM yield on a land basis, an estimated value can be given to a crop. The crop value of the mixtures per hectare was the greatest for the lablab bean mixture ($1570 ha−1) (Table 3) because of high feed nutrient value and high yield. While the velvet bean mixture had a high feed nutrient value (dollar per megagram DM), total mixture yield was relatively low and thus it was not worth as much per hectare. FEEDVAL4 estimates the value of feeds based on the price of different nutrients with CP being a major contributor. So, both feed nutrient value (dollar per megagram DM) and crop value (dollar per hectare) will change with market price for feed ingredients. The lablab bean mixture was the most promising because of additional value associated with higher CP concentration along with no reduction in total DM yield compared with monoculture corn.
View Full Table | Close Full ViewTable 3.
Calculated forage nutrient value of corn–bean mixtures pooled over four environments.
Mixtures
Feed nutrient value
Crop value
$ Mg−1 DM
$ ha−1
Corn
77 c
1480 b
Lablab bean mix
80 a
1570 a
Scarlet runner bean mix
79 b
1490 b
Velvet bean mix
80 a
1500 b
†
Within columns, values are averaged across treatment levels.
‡
Within columns, means followed by different letters are significantly different at P < 0.05.
CONCLUSIONS
While CP concentration was increased in lablab bean– and velvet bean–corn mixtures, fiber and digestibility were compromised compared with monoculture corn. Increased bean concentration in the velvet bean mixture negatively affected calculated milk production per megagram forage (70 kg Mg−1 less) and milk per hectare (2100 kg ha−1 less). In contrast, the lablab bean mixture was not different from monoculture corn in terms of calculated milk per hectare. Lablab bean proved to have the best potential as an intercrop with corn in these experiments. Yield of the lablab bean mixture was not different from monoculture corn and had a calculated feed nutrient value higher than monoculture corn. These experiments show that lablab bean grown with corn has the greatest potential of the three beans to increase CP concentration above monoculture corn without compromising forage yield or calculated milk hectare and potentially increasing crop value (dollar per hectare) in the northern United States. On the basis of this study, the additional costs connected with bean seeds, machinery, and labor costs associated with a separate operation must be less than $90 ha−1 for the lablab bean mixture to be a profitable alternative to monoculture corn.
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