There is an inextricable link between energy production and food/feed/fiber cultivation with available water resources. Currently in the United States, agriculture represents the largest sector of consumptivewater usemaking up 80.7%of the total. Electricity generation in the U.S. is projected to increase by 24 % in the next two decades and globally, the production of liquid transportation fuels are forecasted to triple over the next 25-years, having significant impacts on the import/export market and global economies. The tension between local water supply and demand across water use sectors needs to be evaluated with regards to risk evaluation and planning. To this end, we present a systematic method to spatially and temporally disaggregate nationally available 5-year county-scalewater use data to amonthly 1/8° scale.Our study suggests that while 81.9 % of the U.S. exhibits unstressed local conditions at the annual scale, 13.7 % is considered water scarce; this value increases to 17.3 % in the summer months. The use of mean annualwater scarcity at a coarser basin scale (~373,000 ha)was found to mask information critical for water planning whereas finer spatiotemporal scales revealed local areas that are water stressed or water scarce. Nationally, ~1%of these Bunstressed^ basins actually contained water stressed or water scarce areas equivalent to at least 30 % and 17 %, respectively, of the basin area. These percentages increase to 34 % and 48 % in the summer months. Additionally, 15 % of basins classified as "unstressed" contained water scarce areas in excess of 10 % during the summer.
Increasing demand for crop-based biofuels, in addition to other human drivers of land use, induces direct and indirect land use changes (LUC). Our system dynamics tool is intended to complement existing LUC modeling approaches and to improve the understanding of global LUC drivers and dynamics by allowing examination of global LUC under diverse scenarios and varying model assumptions. We report on a small subset of such analyses. This model provides insights into the drivers and dynamic interactions of LUC (e.g., dietary choices and biofuel policy) and is not intended to assert improvement in numerical results relative to other works.
Demand for food commodities are mostly met in high food and high crop-based biofuel demand scenarios, but cropland must expand substantially. Meeting roughly 25% of global transportation fuel demand by 2050 with biofuels requires >2 times the land used to meet food demands under a presumed 40% increase in per capita food demand. In comparison, the high food demand scenario requires greater pastureland for meat production, leading to larger overall expansion into forest and grassland. Our results indicate that, in all scenarios, there is a potential for supply shortfalls, and associated upward pressure on prices, of food commodities requiring higher land use intensity (e.g., beef) which biofuels could exacerbate.
Biofuels are presented in rich countries as a solution to two crises: the climate crisis and the oil crisis. But they may not be a solution to either, and instead are contributing to a third: the current food crisis.
Meanwhile the danger is that they allow rich-country governments to avoid difficult but urgent decisions about how to reduce consumption of oil, while offering new avenues to continue expensive support to agriculture at the cost of taxpayers. In the meantime, the most serious costs of these policies – deepening poverty and hunger, environmental degradation, and accelerating climate change – are being ‘dumped’ on developing countries.
Crop intensification is often thought to increase greenhouse gas (GHG) emissions, but studies in which crop management is optimized to exploit crop yield potential are rare. We conducted a field study in eastern Nebraska, USA to quantify GHG emissions, changes in soil organic carbon (SOC) and the net global warming potential (GWP) in four irrigated systems: continuous maize with recommended best management practices (CC-rec) or intensive management (CC-int) and maize–soybean rotation with recommended (CS-rec) or intensive management (CS-int). Grain yields of maize and soybean were generally within 80–100% of the estimated site yield potential. Large soil surface carbon dioxide (CO2) fluxes were mostly associated with rapid crop growth, high temperature and high soil water content. Within each crop rotation, soil CO2 efflux under intensive management was not consistently higher than with recommended management. Owing to differences in residue inputs, SOC increased in the two continuous maize systems, but decreased in CS-rec or remained unchanged in CS-int. N2O emission peaks were mainly associated with high temperature and high soil water content resulting from rainfall or irrigation events, but less clearly related to soil NO3-N levels. N2O fluxes in intensively managed systems were only occasionally greater than those measured in the CC-rec and CS-rec systems. Fertilizer-induced N2O emissions ranged from 1.9% to 3.5% in 2003, from 0.8% to 1.5% in 2004 and from 0.4% to 0.5% in 2005, with no consistent differences among the four systems. All four cropping systems where net sources of GHG. However, due to increased soil C sequestration continuous maize systems had lower GWP than maize–soybean systems and intensive management did not cause a significant increase in GWP. Converting maize grain to ethanol in the two continuous maize systems resulted in a net reduction in life cycle GHG emissions of maize ethanol relative to petrol-based gasoline by 33–38%. Our study provided evidence that net GHG emissions from agricultural systems can be kept low when management is optimized toward better exploitation of the yield potential. Major components for this included (i) choosing the right combination of adopted varieties, planting date and plant population to maximize crop biomass productivity, (ii) tactical water and nitrogen (N) management decisions that contributed to high N use efficiency and avoided extreme N2O emissions, and (iii) a deep tillage and residue management approach that favored the build-up of soil organic matter from large amounts of crop residues returned.
PEATSim (Partial Equilibrium Agricultural Trade Simulation) is a dynamic, partial equilibrium, mathematical-based model that enables users to reach analytical solutions to problems, given a set of parameters, data, and initial
conditions. This theoretical tool developed by ERS incorporates a wide range of domestic and border policies that enables it to estimate the market and trade effects of policy changes on agricultural markets. PEATSim captures
the economic behavior of agricultural producers, consumers, and markets in a global framework. It includes variables for production of crops and livestock activities, consumption, exports, imports, stocks, world prices, and domestic producer and consumer prices.
USDA Agricultural Projections for 2011-20, released in February 2011, provide longrun projections for the farm sector for the next 10 years. These annual projections cover agricultural commodities, agricultural trade, and aggregate indicators of the sector, such as farm income and food prices.
Important assumptions for the projections include:
* U.S. and world economic growth move back toward longrun steady increases in the aftermath of the global financial crisis and economic recession.
* Although global population gains continue to slow, growth in most developing countries remains above that in the rest of the world.
* Population gains in developing countries, along with higher incomes, increased urbanization, and expansion of the middle class, are particularly important for growth in global food demand.
* Continued expansion of biofuels further adds to world demand for agricultural products.
Key results in the projections include:
* Recent price increases for many farm commodities underlie record projected levels of U.S. agricultural exports and U.S. net farm income in 2011.
* Prices for major crops decrease in the early years of the projections as global production responds to current high prices.
* World economic growth and demand for biofuels combine to support longer run increases in consumption, trade, and prices for agricultural products.
* Thus, following the near-term declines, prices for corn, wheat, oilseeds, and many other crops remain historically high.
* After near-term reductions from projected 2011 records, the value of U.S. agricultural exports and net farm income each rise through the rest of the decade.
Agricultural markets often feature significant transport costs and spatially distributed production and processing which causes spatial imperfect competition. Spatial economics considers the firms’ decisions regarding location and spatial price strategy separately, usually on the demand side, and under restrictive assumptions. Therefore, alternative approaches are needed to explain, e.g., the location of new ethanol plants in the U.S. at peripheral as well as at central locations and the observation of different spatial price strategies in the market. We use an agent-based simulation model to analyze location and spatial pricing in a general model under multi-firm competition, two-dimensional space, and a continuum of potential price strategies. The results show, e.g., that depending on the location of a processor, different price strategies can be observed, spatial price discrimination can increase with the number of competitors, and elasticity in the producers’ supply functions can be identified as stabilizing factor of processor’s location.
This database contains current and historical official USDA data on production, supply and distribution of agricultural commodities for the United States and key producing and consuming countries.
This model was developed at Idaho National Laboratory and focuses on crop production. This model is an agricultural cultivation and production model, but can be used to estimate biomass crop yields.
The Decision Support System for Agriculture (DSS4Ag) is an expert system being developed by the Site-Specific Technologies for Agriculture (SST4Ag) precision farming research project at the INEEL. DSS4Ag uses state-of-the-art artificial intelligence and computer science technologies to make spatially variable, site-specific,
economically optimum decisions on fertilizer use. The DSS4Ag has an open architecture that allows for external input and addition of new requirements and integrates its results with existing agricultural systems’ infrastructures. The DSS4Ag reflects a paradigm shift in the information revolution in agriculture that is precision farming.