Article

Interannual variability of crop residue potential in the north central region of the United States

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Abstract

Crop residue is potentially a major biomass feedstock for bio-based industries. Spatial and interannual variability of crop residue yield potential in relation to climatic variability in average of daily mean temperature and total precipitation during crop growing season at regional scale has not previously been investigated. Crop yield data were used to estimate crop residue yield potential and quantify its spatial and temporal variability across the North Central Region of the USA. A correlation analysis was also conducted to examine the relationship between temporal stability of crop residue yield and climatic variability. Temporal variability in crop residue and climate parameters was quantified by the coefficient of variation (CV). Based on this observational study, the counties in the south- eastern part of the North Central Region were observed to have relatively stable crop residue yield potential and also have a relatively low CV of average of daily mean temperature and total precipitation during the crop growing season. The CV of crop residue yield potential was positively correlated with the CVs of average of daily mean temperature and total precipitation. These findings highlight the influences of climatic variability on the spatial and temporal patterns of crop residue yield potential, and emphasize that these factors should be taken into account when developing regional strategies for sustainable bioenergy production.

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Major increases in crop yields will be required to meet the future demand for food worldwide, yet changes in climate and diminishing returns from technological advances may limit the ability of many regions to achieve the necessary gains (1, 2). Many researchers have predicted the effect of future climate changes on crop production using a combination of field studies and models (3), but there has been little evidence relating decadal-scale climate change to large-scale crop production. Here, we show that recent trends in temperature have increased the productivity of the two major U.S. crops and that accounting for climate significantly reduces the perceived gains due to management and other factors.
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Sustainable aboveground crop biomass harvest estimates for cellulosic ethanol production, to date, have been limited by the need for residue to control erosion. Recently, estimates of the amount of corn (Zea mays L.) stover needed to maintain soil carbon, which is responsible for favorable soil properties, were reported (5.25-12.50 Mg ha(-1)). These estimates indicate stover needed to maintain soil organic carbon, and thus productivity, are a greater constraint to environmentally sustainable cellulosic feedstock harvest than that needed to control water and wind erosion. An extensive effort is needed to develop advanced cropping systems that greatly expand biomass production to sustainably supply cellulosic feedstock without undermining crop and soil productivity.
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A global assessment of the potential impact of climate change on world food supply suggests that doubling of the atmospheric carbon dioxide concentration will lead to only a small decrease in global crop production. But developing countries are likely to bear the brunt of the problem, and simulations of the effect of adaptive measures by farmers imply that these will do little to reduce the disparity between developed and developing countries.
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Contract No. DE-AC36-99-GO10337NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Available electronically at
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The U.S. Renewable Fuel Standard calls for 136 billion liters of renewable fuels production by 2022. Switchgrass (Panicum virgatum L.) has emerged as a leading candidate to be developed as a bioenergy feedstock. To reach biofuel production goals in a sustainable manner, more information is needed to characterize potential production rates of switchgrass. We used switchgrass yield data and general additive models (GAMs) to model lowland and upland switchgrass yield as nonlinear functions of climate and environmental variables. We used the GAMs and a 39-year climate dataset to assess the spatio-temporal variability in switchgrass yield due to climate variables alone. Variables associated with fertilizer application, genetics, precipitation, and management practices were the most important for explaining variability in switchgrass yield. The relationship of switchgrass yield with climate variables was different for upland than lowland cultivars. The spatio-temporal analysis showed that considerable variability in switchgrass yields can occur due to climate variables alone. The highest switchgrass yields with the lowest variability occurred primarily in the Corn Belt region, suggesting that prime cropland regions are the best suited for a constant and high switchgrass biomass yield. Given that much lignocellulosic feedstock production will likely occur in regions with less suitable climates for agriculture, interannual variability in yields should be expected and incorporated into operational planning.
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The first two volumes are complete revisions of Kendall's two volumes, written in 1943 and 1946. Harvard Book List (edited) 1971 #77 (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Crop residues are a potential source for biofuel production. Yet, impacts of removal of corn (Zea mays L.) stover and other residues as biofuel feedstocks on micro-scale soil properties affecting the behavior of the whole soil are not well understood. Data on both macro- and micro-scale soil properties under different scenarios of stover management are needed to define the threshold levels of stover removal. Previous studies on stover removal impacts on soil properties have primarily focused on macroscale properties and little or not on microscale properties. Thus, this study was designed to assess impacts of annual stover removal for 3 consecutive years at rates of 0, 25, 50, 75, and 100% on soil physical properties at the aggregate level under no-tillage (NT) continuous corn systems in a Rayne silt loam (RSL) (fine-loamy, mixed, active, mesic Typic Hapludult) with 10% slope, Celina silt loam (CSL) (fine, mixed, active, mesic Aquic Hapludalfs) with 2% slope, and Hoytville clay loam (HCL) (fine, illitic, mesic Mollic Epiaqualfs) with < 1% slope in Ohio. Aggregates were sensitive to stover removal particularly in the 0- to 10-cm soil depth. Stover removal reduced aggregate stability, tensile strength (TS), water retention (WR), and subcritical water repellency, while it increased water sorptivity but had no effect on pore-size distribution within an aggregate. The interaction of aggregate stability, strength, and subcritical water repellency with aggregate water potential was highly significantly, indicating that the magnitude of changes in aggregate structural properties due to stover removal depended on the variable antecedent soil water conditions. The aggregate stability and strength decreased while water repellency increased with increasing water potential. Impacts of stover removal on the clayey soils were equal to or higher than those on silt loam soils. Removal at rates ≥ 25% reduced the raindrop kinetic energy (KE) required to break aggregates by 13 times at HCL, while removal at rates ≥ 50% reduced the KE by 2 to 3 times at RSL and CSL for air-dry aggregates. The KE and TS decreased with increasing soil water potentials. The TS was reduced by 10% to 30% at all water potentials at RSL, by 20% to 35% between − 0.1 and − 1.5 MPa, and by 2.2 times at − 166 MPa at CSL, and by 2 to 3 times between − 1.5 and − 166 MPa at HCL under complete stover removal. Removal of stover at rates ≥ 50% reduced subcritical water repellency by 2 to 10 times in all soils. Overall, stover removal altered micro-scale soil properties, and complete stover removal had the most detrimental effects. Based on the data from previous studies on macroscale soil properties and this study, stover removal adversely affects both macro- and micro-scale soil properties.
Article
Mechanisms that explain spatial variability and trends in agricultural productivity at the regional scale are not well understood. Statistical approaches may be used to relate crop yields and trends in crop yields to changes in the economic and bio-physical environment. However, potential yield-explaining variables tend to confound at the regional scale due to strong correlations between these variables, which complicates the interpretation of such empirically derived relationships. In this paper, we assess relationships between different physical and economic variables and yields and trends in yields at the regional scale along a climatic gradient in Europe. We assess the extent of confounding (i.e. confusing the roles of different variables due to strong correlations) among these variables and the associated risk for explaining yield variability and trends in yields.
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Crop production was simulated with the Erosion Productivity Impact Calculator (EPIC) for five representative farms in the Midwestern USA under a variety of climate scenarios. The impact on yields and water use in corn, soybean, winter wheat and sorghum was simulated over a range of temperature, precipitation, solar radiation, humidity and atmospheric carbon dioxide concentration ([CO2]) conditions. Stomatal resistance and leaf area index, plant physiological factors affected by changes in [CO2], were also varied in some of the simulations. Changes in each of these variables altered crop yields and water use. Increases in temperature accelerated the phenological development for all crops, shortened time to maturity, lowered yields, and decreased water use efficiency. Changes in precipitation and vapor pressure affected crop yield and water use by altering the degree of water stress experienced by the crop. For all crops, changes in precipitation and vapor pressure were positively correlated with changes in yield. Changes in solar radiation, which affect the amount of photosynthetically active radiation captured by the plant, were positively correlated with changes in crop yield. Increases in [CO2] and consequent increases in leaf area index and stomatal resistance increased crop yield and water use efficiency, lessening any negative impacts of changes in temperature, precipitation, vapor pressure and solar radiation and amplifying their positive effects. Interactions between different climate variables resulted in crop yield responses ranging from a multiplicative decrease when humidity and precipitation are decreased.to a reduction in crop yield when solar radiation is increased and precipitation decreased. The demonstrated interactions of climatic factors indicate that future studies of climate change impacts should consider the full spectrum of climate variables and changes in atmospheric CO2 and not just temperature and precipitation.
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Crop growth models are essentially site-based, and use of such models for assessing regional productivity of crops requires methods for aggregating over space. Different method for aggregating simulated county and national crop yields for winter wheat (Triticum aestivum L.) in Denmark were tested using a crop simulation model (CLIMCROP), which was run with and without irrigation for a range of soil types and climatic conditions. The aggregated county or national yield was calculated by summing simulated yield of each category multiplied by the area, they represent. Ten different combinations of scales of climate and soil data were used. The wheat area was distributed between the different soil types using either a uniform distribution or a distribution that gave preference to soils with high water-holding capacity. The simulated results were compared with Danish county and national yield statistics for winter wheat from the period 1971–1997. There was, in general, a poor relationship between simulated and observed yields when the observed yields had been detrended to remove the technology effect. A larger fraction of the inter-annual variability was captured by the model on the loamy soils compared with the sandy soils. The model was able to capture most of the spatial variation in observed yields, except at the coarsest resolutions of the soil data. The finest resolution of soil and climate data gave a better fit of simulated to observed spatial autocorrelation in yield. The results indicate that upscaling of simulated productivity of crops for Danish conditions requires a spatial resolution of soil data of or finer. A single climate station may be sufficient if only national yields are estimated, but more stations are required, if regional yields are to be estimated. Consideration should also be given to the distribution of crop area on the different soil types.
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The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided.
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CropSyst is a multi-year, multi-crop, daily time step cropping systems simulation model developed to serve as an analytical tool to study the effect of climate, soils, and management on cropping systems productivity and the environment. CropSyst simulates the soil water and nitrogen budgets, crop growth and development, crop yield, residue production and decomposition, soil erosion by water, and salinity. The development of CropSyst started in the early 1990s, evolving to a suite of programs including a cropping systems simulator (CropSyst), a weather generator (ClimGen), GIS-CropSyst cooperator program (ArcCS), a watershed model (CropSyst Watershed), and several miscellaneous utility programs. CropSyst and associated programs can be downloaded free of charge over the Internet. One key feature of CropSyst is the implementation of a generic crop simulator that enables the simulation of both yearly and multi-year crops and crop rotations via a single set of parameters. Simulations can last a fraction of a year to hundreds of years. The model has been evaluated in many world locations by comparing model estimates to data collected in field experiments. CropSyst has been applied to perform risk and economic analyses of scenarios involving different cropping systems, management options, and soil and climatic conditions. An extensive list of references related to model development, evaluation, and application is provided.