Ranae Dietzel

Trade-offs among agronomic, energetic, and environmental performance characteristics of corn and prairie bioenergy cropping systems

Cellulosic bioenergy production provides opportunities to utilize a range of cropping systems that can enhance the multifunctionality of agricultural landscapes. In a 9‐ha field experiment located on fertile land in Boone County, IA, USA, we directly compared a corn‐soybean rotation harvested for grain, continuous corn harvested for grain and stover, continuous corn harvested for grain and stover with a rye cover crop, newly reconstructed prairie harvested for biomass and fertilized with nitrogen, and unfertilized newly reconstructed prairie harvested for biomass. Comparisons were made using four performance indicators: harvestable yield, net energy balance (NEB), root production, and nutrient balances. We found trade‐offs among systems in terms of the measured performance indicators. Continuous corn systems were the highest yielding, averaging 13 Mg ha−1 of harvested biomass (grain plus stover), whereas fertilized and unfertilized prairies produced the least harvested biomass at 8.8 and 6.5 Mg ha−1, respectively. Mean NEBs were highest in continuous corn systems at 45.1 GJ ha−1, intermediate in the corn‐soybean rotation at 28.6 GJ ha−1, and lowest in fertilized and unfertilized prairies at 11.4 and 10.5 GJ ha−1, respectively. Concomitant with the high yields of the continuous corn systems were the large nutrient requirements of these systems compared to the prairie systems. Continuous corn with rye required three times more nitrogen inputs than fertilized prairie. Root production, on the other hand, was on average seven times greater in the prairie systems than the annual crop systems. On highly fertile soils, corn‐based cropping systems are likely to play an important role in maintaining the high productivity of agricultural landscapes, but alternative cropping systems, such as prairies used for bioenergy production, can produce substantial yield, require minimal externally derived inputs, and can be incorporated into the landscape at strategic locations to maximize the production of other ecosystem services.

How efficiently do corn‐ and soybean‐based cropping systems use water? A systems modeling analysis

Agricultural systems are being challenged to decrease water use and increase production while climate becomes more variable and the worlds population grows. Low water use efficiency is traditionally characterized by high water use relative to low grain production and usually occurs under dry conditions. However, when a cropping system fails to take advantage of available water during wet conditions, this is also an inefficiency and is often detrimental to the environment. Here, we provide a systems-level definition of water use efficiency (sWUE) that addresses both production and environmental quality goals through incorporating all major system water losses (evapotranspiration, drainage, and runoff). We extensively calibrated and tested the Agricultural Production Systems sIMulator (APSIM) using 6 years of continuous crop and soil measurements in corn- and soybean-based cropping systems in central Iowa, USA. We then used the model to determine water use, loss, and grain production in each system and calculated sWUE in years that experienced drought, flood, or historically average precipitation. Systems water use efficiency was found to be greatest during years with average precipitation. Simulation analysis using 28 years of historical precipitation data, plus the same dataset with ± 15% variation in daily precipitation, showed that in this region, 430 mm of seasonal (planting to harvesting) rainfall resulted in the optimum sWUE for corn, and 317 mm for soybean. Above these precipitation levels, the corn and soybean yields did not increase further, but the water loss from the system via runoff and drainage increased substantially, leading to a high likelihood of soil, nutrient, and pesticide movement from the field to waterways. As the Midwestern United States is predicted to experience more frequent drought and flood, inefficiency of cropping systems water use will also increase. This work provides a framework to concurrently evaluate production and environmental performance of cropping systems.

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward agronomic, economic, and environmental sustainability.

The future of agriculture and society in Iowa: four scenarios

Iowa is a leader in crop and livestock production, but its high productivity has had concomitant negative environmental and societal impacts and large requirements for fossil-fuel-derived inputs. Maintaining agricultural productivity, economic prosperity and environmental integrity will become ever more challenging as the global demand for agricultural products increases and the resources needed become increasingly limited. Here we present four scenarios for Iowa in 2100, based on combinations of differing goals for the economy and differing energy availability. In scenarios focused on high material throughput, environmental degradation and social unrest will increase. In scenarios with a focus on human and environmental welfare, environmental damage will be ameliorated and societal happiness will increase. Movement towards a society focused on human and environmental welfare will require changes in the goals of the economy, whereas no major changes will be needed to maintain focus on high throughput. When energy sources are readily available and inexpensive, the goals of the economy will be more easily met, whereas energy limitations will restrict the options available to agriculture and society. Our scenarios can be used as tools to inform people about choices that must be made to reach more desirable futures for Iowa and similar agricultural regions.