The study focuses on economic and food security implications of projected climate change on Malian agriculture sector. Climate change projections made by two global circulation models are considered. The analysis focuses on the effects on crops, forages, and livestock and the resultant effects on sectoral economics and risk of hunger in Mali. Results show that under climate change, crop yield changes are in the range of minus 17% to plus 6% at national level. Simultaneously, forage yields fall by 5 to 36% and livestock animal weights are reduced by 14 to 16%. The resultant economic losses range between 70 to $142 million, with producers gaining, but consumers losing. The percentage of population found to be at risk of hunger rises from a current estimate of 34% to an after climate change level of 64% to 72%. A number of policy and land management strategies can be employed to mitigate the effects of climate change. In particular, we investigate the development of heat resistant cultivars, the adoption of existing improved cultivars, migration of cropping pattern, and expansion of cropland finding that they effectively reduce climate change impacts lowering the risk of hunger to as low as 28%.

Model simulations performed representing dairies in a 93000 ha watershed in north central Texas suggest that manure incorporation results in reduced phosphorus (P) losses at relatively small to moderate cost to producers. Simulated manure incorporation with a tandem disk on fields double-cropped with sorghum/winter wheat resulted in up to 33, 45, and 37% reductions in per hectare sediment-bound, soluble, and total P losses in edge-of-field runoff, relative to simulated surface manure applications. The effects of incorporation were evaluated at three different manure application rates. On aggregate across all three manure application rates, significant declines in P losses were obtained with incorporation except for sediment-bound P losses under the N-based manure application rate scenario. We found that the practice of incorporating manure shortly after it has been broadcast on the soil surface could help reduce P losses in such situations where P-based rates alone prove inadequate. The cost the producer incurs when manure is incorporated is on average about 1% of net returns when manure is applied at the N rate and 2-3% when it is applied at alternative P-based rates. In practice the costs could be lower because producers may substitute the manure incorporation operation for a tandem disk operation performed prior to manure application. As more and more dairy producers switch to the use of sorghum and corn silage in dairy rations and consequent on-farm production of these forages, the practice of manure incorporation may help to reduce phosphorus losses resulting from dairy manure applications to fields with these forage crops.

Flooding is the most costly and destructive natural disaster in the U.S. From 1990 to 1997, floods caused an average of $5.7 billion in damages and 98 fatalities per year. The apparent increase of flood events and associated damage, is explained mainly by increasing occupation of flood plains by agriculture, transportation infrastructure, industry, and urban development. Another important factor in areas subject to floods, is inadequate management of watersheds which includes inappropriate land use and/or use of inappropriate agricultural production practices. A third factor, in many parts of the country seems to be the higher frequency and, severity of extreme precipitation events. This study has two main objectives: First, to identify areas in the U.S. with evidences of upward trends in the occurrence of extreme precipitation events. And second, to suggest farmland management practices for interception of runoff for mitigation of floods. Statistical analysis for detection of extreme event trends was applied on data from approximately eight thousand weather stations, this high resolution network of stations allows analysis of precipitation interaction with very localized properties such as soil properties and watershed characteristics. Stations with evidences of positive trends were clustered or arranged in patterns in many places across the country. Areas associated with clusters or patterns were selected to simulate three management practices: furrow diking, contour terraces, and combination of the two previous practices. Simulations were performed at subasin scale with APEX (Agricultural Policy/Environmental eXtender). Efficacy and feasibility of practices varied according to magnitude of precipitation events, and soil and watershed characteristics.

In May 1991, the EPA proposed management measures for controlling erosion in the coastal zone regions of the U.S. One proposed management measure for cropland soil erosion and sedimentation would require producers to limit cropland soil erosion to the lesser of T (soil loss tolerance) or that occurring with conservation tillage. This study estimated the farm level impacts on cropping patterns, soil erosion, and economic returns associated with selected coastal zones complying with this proposed regulation. Three sites were selected for analysis: (1) Texas Coast, (2) Coastal Georgia, and (3) Northern Indiana. The method of analysis was a farm profit maximization program. Farming practices data were incorporated into the models, and the 1987 National Resources Inventory (NRI) data base provided regional hectares of each crop on each of four land types with different soil erodibility. The results indicate that the Texas Coastal Bend is currently within compliance, thus, there would be little to no expected impact from the proposed guidelines. For Coastal Georgia and Northern Indiana, row crops on erodible productive land would be expected to shift with bay on less productive land, giving a 25 percent and 43 percent reduction in sheet and rill erosion for the two areas. These shifts in cropping patterns would result in about 3.25 percent and 3.59 percent reduction in net returns for the case farms.

The adaptation of a crop simulation model to deal with the impacts of rising CO2 and climate change is described in this paper. Algorithms that represent the direct effects of atmospheric CO2 on crop photosynthetic efficiency and water use were developed for use with the erosion productivity impact calculator (EPIC), a mechanistic crop simulation model. Representative farms were designed to reflect the major cropping systems in the MINK (Missouri-Iowa-Nebraska-Kansas) region and data were assembled to simulate them in EPIC. Climate data were compiled to represent conditions under the control (1951–1980) and analog (1931–1940) climates. Actual daily temperature and precipitation data from a number of climatological stations across the MINK region were used in the simulations. Daily values of solar radiation, relative humidity, and wind speed were simulated stochastically from monthly First Order Weather Station records.

That atmospheric CO2 concentration is increasing is well established, and it is generally well accepted that this increase will have beneficial effects on plant productivity. What remains most uncertain is the nature and magnitude of the climatic changes that will occur as a result of the increase of carbon dioxide and other radiatively active trace gases. Thus, it is difficult to predict the combined impact of increasing atmospheric carbon dioxide on agricultural productivity. A comprehensive cropping system simulation model, EPIC, was used in a sensitivity analysis of crop growth response to the combined effects of CO2 concentration increase and CO2-induced climate change.Maize, soybean and wheat cropping systems in the midwestern USA were studied.

Cation-exchange capacity (CEC) is an important soil property in describing nutrient availability for plant growth. Measurements of CEC, however, are often not available or have been measured using different analytical methods. The need, therefore, exists to develop alternative procedures to predict CEC from accessory soil properties. In this study, regression analysis was used to examine the relationships between CEC and clay (CLAY), organic carbon (OC), and other soil properties. Multiple regressions indicated that CLAY, OC, and soil pH accounted for up to 51% of the variation in CEC for all soils (n = 37 921). For soil orders, CLAY and OC accounted for up to 67% of the variation in CEC for Alfisols, Inceptisols, Mollisols, and Vertisols, and up to 78% of the variation in CEC for Entisols and Spodosols. The OC alone accounted for up to 73% of the variation in CEC for Spodosols. Poor predictions of CEC resulted from CLAY for Aridisols and Vertisols, indicating that factors other than CLAY interfered with accurate predictions of CEC.

Knowledge of soil water availability for plant growth is vital for the development of plant growth simulation models. Data on soil water availability are often not available because field and laboratory measurements of soil water content are time-consuming and tedious. The objective of this study was to develop alternative procedures to predict water content at -10 kPa (UL10), -33 kPa (U133), -1500 kPa (LL), and the potential available water capacity (AWC) from easily and routinely available soil properties. Multiple regression equations for soil orders of Soil Taxonomy were developed using a database containing information of about 12,000 pedons of the continental U.S., Hawaii, Puerto Rico, and some foreign countries. Regression equations with bulk density, sand, silt, clay, and organic carbon contents accounted for up to 83% of the variation in UL10 for all orders except Ultisols. For Ultisols, sand content accounted for up to 90% of the variation in UL10. Equations with clay and organic carbon contents accounted for up to 75% of the variability in UL33 for all except Aridisols, Oxisols, Vertisols, and Spodosols. For these four orders, equations with bulk density, clay, silt, and sand contents accounted for up to 81% of the variation in UL33.LL was linearly related to clay content. Clay content accounted for up to 91% of the variation in LL for all but Oxisols and Vertisols. More accurate predictions of AWC resulted when AWC was computed from UL10 and LL water content data. Equations with bulk density alone or bulk density plus silt and/or sand contents accounted for up to 83% of the variation in AWC for all except Entisols, Inceptisols, and Spodosols.

In the early 1980s, a mathematical model called EPIC (Erosion-Productivity Impact Calculator) was developed as part of the United States Soil and Water Resources Conservation Act to assess the relationship between soil erosion by wind or water and crop productivity throughout the United States. The model uses a daily time step to simulate weather, hydrology, soil temperature, erosion-sedimentation, nutrient cycling, tillage, crop management and growth, and field-scale costs and returns. Since 1985, an interactive data entry system; flexible graphical output utilities; extensive soil and weather generation databases; crop and tillage parameter databases; and alternative methods to simulate erosion, weather, irrigation, fertilization and tillage have been added. The expanded model and associated software can now be considered an operational, microcomputer-based, decision support system to analyze the productivity and sustainability of complex cropping systems.

Soil physical, chemical, and taxonomic data of about 12,000 pedons from the continental U.S., Hawaii, Puerto Rico, and some foreign countries were used to develop multiple regression equations to estimate sum of exchangeable bases (SUMBAS) and base saturation (BS). Soils were grouped according to their taxonomic classification at order and suborder categories. Multiple regression equations using organic carbon and clay contents, and soil pH in water ratio 1:1 accounted for more than 50% of the variation in SUMBAS in 11 of 35 suborders included in this study. Regression equations using organic carbon and clay contents, pH, 1M KC1 extractable A1 or percent A1 saturation, and cation exchange capacity accounted for more than 70% of the variation in SUMBAS in 18 of 35 suborders. Percent A1 saturation and organic carbon content were negatively related to BS. These two parameters accounted for more than 70% of the variation in BS in 16 of 32 suborders. Regression equations using soil pH alone, in turn, accounted for more than 70% of the variation in BS in 4 of 6 suborders with a formative element Aquic in their taxonomic names.

Management of complex crop rotations in southern France requires accurate assessment of the effects of irrigation, nitrogen fertilization, and the previous crop on crop growth and yield. The Erosion-Productivity Impact Calculator (EPIC) Cropping Systems model simulates the effects of weather, soil characteristics, tillage, fertilization, irrigation and other management practices on crops grown in complex rotations. The present study was conducted to evaluate EPIC's ability to simulate growth and yield of corn (Zea mays L.), grain sorghum (Sorghum bicolor (L.) Moench), sunflower (Helianthus annuus L.), soybean (Glycine max (L.) Merr.) and wheat (Triticum aestivum L.) when grown in rotations at three levels of management inputs, including three levels each of fertilizer, irrigation and tillage, over a 5-year period.

ABSTRACT: Soil erosion has both short- and long-term impacts. A logical methodology for addressing the multigeneration impacts of alternative conservation systems was developed. The proposed methodology could be used by policymakers to establish erosion control goals based on the known technologies for conservation and crop production at the present time. As technologies change in the future, periodic reassessments of goals could be made using the same methodology.

ABSTRACT A mathematical model called EPIC (Erosion-Productivity Impact Calculator) was developed to determine the relationship between soil erosion and soil productivity throughout the U.S. EPIC continuously simulates the processes involved simultaneously and realistically, using a daily time step and readily available inputs. Since erosion can be relatively slow process, EPIC is capable of simulating hundreds of years if necessary. EPIC is generally applicable, computationally efficient, and capable of computing the effects of management changes on outputs. The model must be comprehensive to define the erosion-productivity relationship adequately. EPIC is composed of physically-based components for simulating erosion, plant growth, and related processes and economic components for assessing the cost of erosion, determining optimal management strategies, etc. The EPIC components include weather simulation, hydrology, erosion-sedimentation, nutrient cycling, plant growth, tillage, soil temperature, economics, and plant environment control. Typical results are presented for 15 of the 163 tests performed in the continental U.S. and Hawaii. These results generally indicate that EPIC is capable of simulating erosion and crop growth realistically.

THE mathematical model EPIC (Erosion-Productivity Impact Calculator) was developed recently to determine the relationship between soil erosion and soil productivity in the United States (6). To accomplish this complex objective, four goals were set in the model development process. The model must be (a) physically based and capable of simulating the processes involved simultaneously and realistically using readily available inputs; (b) capable of simulating hundreds of years, if necessary, because erosion can be a relatively slow process; (c) applicable to a wide range of soils, climates, and crops encountered in the United States; and (d) efficient, convenient to use, and capable of assessing the effects of management changes on erosion and soil productivity. EPIC is composed of physically based components for simulating erosion, plant growth, and related processes. It also includes economic components for assessing the cost of erosion and determining optimal management strategies. …

Estimates of mineralization of soil organic N are often needed to predict N fertilizer requirements of crops. G. Stanford and his collaborators developed laboratory techniques to estimate potentially mineralizable N (NO) in soils and a rate constant for mineralization (k). Their techniques have been used to estimate N mineralization in the field. This study reports the use of stepwise multiple regression techniques to estimate N from soil pH, organic carbon concentration, total N concentration, and soil taxonomy. The regression equation for No accounted for 83% of the variation of No for 90 samples from 67 soils representing eight soil orders. The regression equation for k accounted for 40% of the variation in the rate constant k for 61 samples from 43 soils. These results should aid in the estimation of N mineralization in cases where laboratory measurements of N and k are not available.