Growing Corn in a Computer: The Hybrid Hybrid-Maize Simulation Model and its Application to Production Agriculture

Outline Yield potential and yield gaps To achieve yield potential of an environment Hybrid Hybrid-Maize model Hybrid Hybrid-Maize validation Potential applications EI Lincoln, NE: 2003 Yields What determines spatial variation in corn yield potential in Nebraska? Simulated attainable corn yields in different regions of Nebraska To achieve full climatic site yield potential, management requires: Optimal management: gain in season length Corn yield potential in 2003 Potential applications Summary Outloo

Calibration and Validation of the Hybrid-Maize Crop Model for Regional Analysis and Application over the U.S. Corn Belt

Detailed parameter sensitivity, model validation, and regional calibration of the Hybrid-Maize crop model were undertaken for the purpose of regional agroclimatic assessments. The model was run at both field scale and county scale. The county-scale study was based on 30-yr daily weather data and corn yield data from the National Agricultural Statistics Service survey for 24 locations across the Corn Belt of the United States. The field-scale study was based on AmeriFlux sites at Bondville, Illinois, andMead, Nebraska. By using the one-at-a-time and interaction-explicit factorial design approaches for sensitivity analysis, the study found that the five most sensitive parameters of the model were potential number of kernels per ear, potential kernel filling rate, initial light use efficiency, upper temperature cutoff for growing degree-days’ accumulation, and the grain growth respiration coefficient. Model validation results show that the Hybrid-Maize model performed satisfactorily for field-scale simulations with a mean absolute error (MAE) of 10 bu acre-1 despite the difficulties of obtaining hybrid-specific information. At the county scale, the simulated results, assuming optimal crop management, overpredicted the yields but captured the variability well. A simple regional adjustment factor of 0.6 rescaled the potential yield to actual yield well. These results highlight the uncertainties that exist in applying crop models at regional scales because of the limitations in accessing cropspecific information. This study also provides confidence that uncertainties can potentially be eliminated via simple adjustment factor and that a simple crop model can be adequately useful for regional-scale agroclimatic studies

In-season Prediction of Attainable Maize Yield Using the Hybrid-Maize Model

The Hybrid-Maize Model Real-time Simulation and Yield Forecasting Case Study 1: Irrigated Maize, Lincoln, Nebraska Case Study 2: Rainfed Maize, Oliveros, Argentina Case Study 3: Rainfed Maize, Mead, Nebraska Conclusions Reference

Closing yield gaps for rice self-sufficiency in China

China produces 28% of global rice supply and is currently self-sufficient despite a massive rural-to-urban demographic transition that drives intense competition for land and water resources. At issue is whether it will remain self-sufficient, which depends on the potential to raise yields on existing rice land. Here we report a detailed spatial analysis of rice production potential in China and evaluate scenarios to 2030. We find that China is likely to remain self-sufficient in rice assuming current yield and consumption trajectories and no reduction in production area. A focus on increasing yields of double-rice systems on general, and in three single-rice provinces where yield gaps are relatively large, would provide greatest return on investments in research and development to remain self-sufficient. Discrepancies between results from our detailed bottom-up yield-gap analysis and those derived following a topdown methodology show that the two approaches would result in very different research and development priorities

Estimating yield potential in temperate high-yielding, direct-seeded US rice production systems

Accurate estimation of a crop’s yield potential (Yp) is critical to addressing long-term food security via identification of the exploitable yield gap. Due to lack of field data, efforts to quantify crop yield potential typically rely on crop models. Using the ORYZA rice crop model, we sought to estimate Yp of irrigated rice for two widely used rice varieties (M-206 and CXL745) in three major US rice-producing regions that together represent some of the highest yielding rice regions of the world. Three major issues with the crop model had to be addressed to achieve acceptable simulation of Yp; first, the model simulated leaf area index (LAI) and biomass agreed poorly for all direct-seeded systems using default settings;second, cold-induced sterility and associated yield losses were poorly simulated for environments with a large diurnal temperature variation; lastly, simulated Yp was sensitive to the specified definition of physiological maturity. Except for the simulation of cold-induced sterility, all issues could be remedied within the existing model structure. In contrast, simulation of cold-induced sterility posed a continuing challenge to accurate simulation—one that will likely require changes to ORYZA’s formulation. Estimates of Yp from the modified model were validated against large multi-year data sets of experimental yields covering the majority of US rice production areas. Validation showed the adjusted model simulated Yp well, with most top yields falling within 85% of Yp for both varieties (77% and 78% observed yields within15% of Yp for CXL745 and M-206 respectively). Maximum estimated Yp was 14.3 (range of 8.2–14.5) and14.5 (range of 8.7–15.3) t ha−1for the Southern US and CA, respectively

Estimating yield potential in temperate high-yielding, direct-seeded US rice production systems

Accurate estimation of a crop’s yield potential (Yp) is critical to addressing long-term food security via identification of the exploitable yield gap. Due to lack of field data, efforts to quantify crop yield potential typically rely on crop models. Using the ORYZA rice crop model, we sought to estimate Yp of irrigated rice for two widely used rice varieties (M-206 and CXL745) in three major US rice-producing regions that together represent some of the highest yielding rice regions of the world. Three major issues with the crop model had to be addressed to achieve acceptable simulation of Yp; first, the model simulated leaf area index (LAI) and biomass agreed poorly for all direct-seeded systems using default settings;second, cold-induced sterility and associated yield losses were poorly simulated for environments with a large diurnal temperature variation; lastly, simulated Yp was sensitive to the specified definition of physiological maturity. Except for the simulation of cold-induced sterility, all issues could be remedied within the existing model structure. In contrast, simulation of cold-induced sterility posed a continuing challenge to accurate simulation—one that will likely require changes to ORYZA’s formulation. Estimates of Yp from the modified model were validated against large multi-year data sets of experimental yields covering the majority of US rice production areas. Validation showed the adjusted model simulated Yp well, with most top yields falling within 85% of Yp for both varieties (77% and 78% observed yields within15% of Yp for CXL745 and M-206 respectively). Maximum estimated Yp was 14.3 (range of 8.2–14.5) and14.5 (range of 8.7–15.3) t ha−1for the Southern US and CA, respectively

A case study of field-scale maize irrigation patterns in western Nebraska: implications for water managers and recommendations for hyper-resolution land surface modeling

In many agricultural regions, the human use of water for irrigation is often ignored or poorly represented in land surface models (LSMs) and operational forecasts. Because irrigation increases soil moisture, feedback on the surface energy balance, rainfall recycling, and atmospheric dynamics is not represented and may lead to reduced model skill. In this work, we describe four plausible and relatively simple irrigation routines that can be coupled to the next generation of hyper-resolution LSMs operating at scales of 1 km or less. The irrigation output from the four routines (crop model, precipitation delayed, evapotranspiration replacement, and vadose zone model) is compared against a historical field-scale irrigation database (2008–2014) from a 35 km2 study area under maize production and center pivot irrigation in western Nebraska (USA). We find that the most yield-conservative irrigation routine (crop model) produces seasonal totals of irrigation that compare well against the observed irrigation amounts across a range of wet and dry years but with a low bias of 80mmyr-1. The most aggressive irrigation saving routine (vadose zone model) indicates a potential irrigation savings of 120mmyr-1 and yield losses of less than 3% against the crop model benchmark and historical averages. The results of the various irrigation routines and associated yield penalties will be valuable for future consideration by local water managers to be informed about the potential value of irrigation saving technologies and irrigation practices. Moreover, the routines offer the hyper-resolution LSM community a range of irrigation routines to better constrain irrigation decision-making at critical temporal (daily) and spatial scales (\u3c1 \u3ekm)

Nitrogen response functions targeted to technology extrapolation domains in Ethiopia using CERES-maize

The profitability of fertilizer-N use can be optimized using N response functions specific to climate-based technology extrapolation domains (TED). Crop growth simulation can complement field research for targeting of response functions. The objective of this study was to target maize (Zea mays L.) N response functions to seven TED in Ethiopia through CERES-Maize simulation of continuous maize over 30 yr. The complete factorial set of treatments included seven levels of N in 25 kg ha−1 increments under no-till (NT) and conventional tillage (CT) systems. The CERES-Maize simulated experiments were done for two or three sites per TED. Nitrogen response functions were generated for each TED with tillage-specific functions for three TED with tillage × N interactions. The N rate responses for all TED fit curvilinear to plateau functions but with differing magnitudes and shapes of response. The mean yield with NT was 6% less than with CT, but the difference declined with increased N rate. The economically optimum N rate (EONR) ranged from 65 to 179 and 103 to 243 kg ha−1 for high and low-cost fertilizer-N, respectively. The EONR was 6% less and the profit cost ratio was 11% higher with CT compared to NT, indicating greater fertilizer-N need with NT than with CT. The application of N for maize was highly profitable for all TED. The EONR from CERES-Maize were higher than past field research results. This suggests that the CERES-Maize N response functions were most appropriate for well-managed crop production situations in Ethiopia

Mesoscale Modeling of the Meteorological Impacts of Irrigation during the 2012 Central Plains Drought

In the summer of 2012, the central plains of the United States experienced one of its most severe droughts on record. This study examines the meteorological impacts of irrigation during this drought through observations and model simulations using the Community Land Model coupled to the Weather Research and Forecasting (WRF) Model. A simple parameterization of irrigation processes is added into the WRF Model. In addition to keeping soil moisture in irrigated areas at a minimum of 50% of soil moisture hold capacity, this irrigation scheme has the following new features: 1) accurate representation of the spatial distribution of irrigation area in the study domain by using a MODIS-based land surface classification with 250-m pixel size and 2) improved representation of the time series of leaf area index (LAI) values derived from crop modeling and satellite observations in both irrigated and nonirrigated areas. Several numerical sensitivity experiments are conducted. The WRF-simulated temperature field when including soil moisture and LAI modification within the model is shown to be most consistent with ground and satellite observations, all indicating a temperature decrease of 2–3K in irrigated areas relative to the control run. Modification of LAI in irrigated and dryland areas led to smaller changes, with a 0.2-K temperature decrease in irrigated areas and up to a 0.5-K temperature increase in dryland areas. Furthermore, the increased soil moisture and modified LAI are shown to lead to statistically significant increases in surface divergence and surface pressure and to decreases in planetary boundary layer height over irrigated areas