Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study’s objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASAPOWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method

Student-Conducted Farmer Video Interviews

High school agricultural education teachers have expressed concern about the lack of easily accessible educational materials dealing with contemporary topics in sustainable agriculture. There are numerous textbooks and monographs available for farmers and students at the college level, including the highly practical resources available from the Sustainable Agriculture Research and Education (SARE) book series on soil fertility (Magdoff and van Es, 2010), cover crops (Bowman et al., 2007) and building a farm business (DiGiacomo et al., 2003), among others. Although these are full of color photos and easily accessible graphs and tables, they are still in the print media category. Many of today’s students, accustomed to personal electronic devices and instant access to entertaining (and hopefully educational) video material are more apt to use information from newer formats. As one student said, perhaps in jest, “If it is not online, for me it does not exist.” So we determined to meet high school students where they are. The regional SARE grant committee agreed with our assessment and a modest proposal was approved to develop accessible sustainable agriculture teaching materials for high school students. With the help of experienced Nebraska high school teachers, we selected topics that would supplement their current modules in courses and raise interest by virtually ‘bringing farmers into the classroom’. To add interest for the high school agriculture classes, students were selected to do the interviews. Questions were carefully edited by a member of the SARE grant team (Jenn Simons) and professionally produced by information technology experts at the University of Nebraska-Lincoln. Here are the methods used and results of the project

Use of agro-climatic zones to upscale simulated crop yield potential

Yield gap analysis, which evaluates magnitude and variability of difference between crop yield potential (Yp) or water limited yield potential (Yw) and actual farm yields, provides a measure of untapped food production capacity. Reliable location-specific estimates of yield gaps, either derived from research plots or simulation models, are available only for a limited number of locations and crops due to cost and time required for field studies or for obtaining data on long-term weather, crop rotations and management practices, and soil properties. Given these constraints, we compare global agro-climatic zonation schemes for suitability to up-scale location-specific estimates of Yp and Yw, which are the basis for estimating yield gaps at regional, national, and global scales. Six global climate zonation schemes were evaluated for climatic homogeneity within delineated climate zones (CZs) and coverage of crop area. An efficient CZ scheme should strike an effective balance between zone size and number of zones required to cover a large portion of harvested area of major food crops. Climate heterogeneity was very large in CZ schemes with less than 100 zones. Of the other four schemes, the Global Yield Gap Atlas Extrapolation Domain (GYGA-ED) approach, based on a matrix of three categorical variables (growing degree days, aridity index, temperature seasonality) to delineate CZs for harvested area of all major food crops, achieved reasonable balance between number of CZs to cover 80% of global crop area and climate homogeneity within zones. While CZ schemes derived from two climate-related categorical variables require a similar number of zones to cover 80% of crop area, within-zone heterogeneity is substantially greater than for the GYGA-ED for most weather variables that are sensitive drivers of crop production. Some CZ schemes are cropspecific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with crop rotations rather than single crop species

Yield gap analysis of US rice production systems shows opportunities for improvement

Many assessments of crop yield gaps based on comparisons to actual yields suggest grain yields in highly intensified agricultural systems are at or near the maximum yield attainable. However, these estimates can be biased in situations where yields are below full yield potential. Rice yields in the US continue to increase annually, suggesting that rice yields are not near the potential. In the interest of directing future efforts towards areas where improvement is most easily achieved, we estimated yield potential and yield gaps in US rice production systems, which are amongst the highest yielding rice systems globally. Zones around fourteen reference weather stations were created, and represented 87% of total US rice harvested area. Rice yield potential was estimated over a period of 13–15 years within each zone using the ORYZA(v3) crop model. Yield potential ranged from 11.5 to 14.5 Mg ha−1, while actual yields varied from 7.4 to 9.6 Mg ha−1, or 58–76% of yield potential. Assuming farmers could exploit up to 85% of yield potential, yield gaps ranged from 1.1 to 3.5 Mg ha−1. Yield gaps were smallest in northern California and the western rice area of Texas, and largest in the southern rice area of California, southern Louisiana, and northern Arkansas/southern Missouri. Areas with larger yield gaps exhibited greater annual yield increases over the study period (35.7 kg ha−1 year −1 per Mg yield gap). Adoption of optimum management and hybrid rice varieties over the study period may explain annual yield increases, and may provide a means to further increase production via expanded adoption of current technologies

Understanding Spatial Welfare Impacts of a Grain Ethanol Plant

This study inquires into the spatial welfare impacts of a grain ethanol plant established in an area with a beef feeding industry. Beef feeders, corn farmers, and the ethanol plant interact with each other simultaneously in a dynamic market situation. To date, there are no studies which simultaneously analyze the welfare impacts of an ethanol enterprise on the three major players affected by the existence of a plant. In this market situation, some interesting phenomena have been noted which raise some intriguing questions. Why do plants sell ethanol byproduct feed at prices below corn price, even though studies show the byproduct to be a more valuable feed? Why does a plant, which could ship a non-perishable all over the world, choose instead to produce a perishable product? The answers to these questions are affected by the density of corn production, the density of beef production and the size of an ethanol plant in an area. This paper will shed some light on how these and other factors influence welfare and answers the questions posed above. The answers to these questions are important to the agents affected, but empirical evidence is not available on a sufficiently fine spatial grid to address them. Therefore the approach of this study is to construct a spatial equilibrium model to examine conditions that determine the distribution of welfare benefits from the existence of a plant. The model is driven by the plant’s choice of prices for corn and byproduct so as to minimize net feedstock cost for the plant’s capacity. These prices, and the welfare impacts on corn producers, feedlots and the plant itself, will depend upon transportation costs, the density of corn and beef production and the size of the plant

Spatial Welfare Impacts of a Grain Ethanol Plant

This study inquires into the spatial welfare impacts of a grain ethanol plant established in an area with a beef feeding industry. Corn producers will benefit, but by how much? Why do plants seem to price their animal feed byproduct so low that beef producers may benefit from lower feed costs, despite the higher corn price? Why do ethanol plants in some areas dry all their byproduct feed while in other areas plants sell it all in wet form? How are these outcomes affected by the density of corn production, by the density of feedlots, and by the size of the ethanol plant? The answers to these questions are important to the agents affected, but empirical evidence is not available on a sufficiently fine spatial grid to address them. Therefore the approach of this study is to construct a spatial equilibrium model to examine conditions that determine the distribution of welfare benefits from the existence of a plant. The model is driven by the plant’s choice of prices for corn and byproduct so as to minimize net feedstock cost for the plant’s capacity. These prices, and the welfare impacts on corn producers, feedlots and the plant itself, will depend upon transportation costs, the density of corn and beef production and the size of the plant

Estimating national crop yield potential and the relevance of weather data sources

To determine where, when, and how to increase yields, researchers often analyze the yield gap (Yg), the difference between actual current farm yields and crop yield potential. Crop yield potential (Yp) is the yield of a crop cultivar grown under specific management limited only by temperature and solar radiation and also by precipitation for water limited yield potential (Yw). Yp and Yw are critical components of Yg estimations, but are very difficult to quantify, especially at larger scales because management data and especially daily weather data are scarce. A protocol was developed to estimate Yp and Yw at national scales using site-specific weather, soils and management data. Protocol procedures and inputs were evaluated to determine how to improve accuracy of Yp, Yw and Yg estimates. The protocol was also used to evaluate raw, site-specific and gridded weather database sources for use in simulations of Yp or Yw. The protocol was applied to estimate crop Yp in US irrigated maize and Chinese irrigated rice and Yw in US rainfed maize and German rainfed wheat. These crops and countries account for \u3e20% of global cereal production. The results have significant implications for past and future studies of Yp, Yw and Yg. Accuracy of national long-term average Yp and Yw estimates was significantly improved if (i) \u3e 7 years of simulations were performed for irrigated and \u3e 15 years for rainfed sites, (ii) \u3e 40% of nationally harvested area was within 100 km of all simulation sites, (iii) observed weather data coupled with satellite derived solar radiation data were used in simulations, and (iv) planting and harvesting dates were specified within ± 7 days of farmers actual practices. These are much higher standards than have been applied in national estimates of Yp and Yw and this protocol is a substantial step in making such estimates more transparent, robust, and straightforward. Finally, this protocol may be a useful tool for understanding yield trends and directing research and development efforts aimed at providing for a secure and stable future food supply

Understanding Spatial Welfare Impacts of a Grain Ethanol Plant

A changing world of increasing complexity, fluctuating prices, high energy costs and limited data necessitate creative blending of economic theory and available empirical statistics to understand the welfare impacts in a specific market. In this paper, a programming approach is used in tandem with spatial economic theory to understand the spatial welfare impacts of an ethanol plant established in an area with a beef feeding industry. The study concludes that corn transportation costs are less significant in plant pricing strategy than originally identified by other studies. Local ethanol plant competition is found to explain the lower-than-feed value pricing of ethanol byproducts at the plant. In the study, average welfare effects are calculated for the ethanol plant, corn producers and beef producers under different market situations and changes

Estimating crop yield potential at regional to national scales

World population will increase 35% by 2050, which may require doubling crop yields on existing farm land to minimize expansion of agriculture into remaining rainforests, wetlands, and grasslands. Whether this is possible depends on closing the gap between yield potential (Yp, yield without pest, disease, nutrient or water stresses, or Yw under water-limited rainfed conditions) and current average farm yields in both developed and developing countries. Quantifying the yield gap is therefore essential to inform policies and prioritize research to achieve food security without environmental degradation. Previous attempts to estimate Yp and Yw at a global level have been too coarse, general, and opaque. Our purpose was to develop a protocol to overcome these limitations based on examples for irrigated rice in China, irrigated and rainfed maize in the USA, and rainfed wheat in Germany. Sensitivity analysis of simulated Yp or Yw found that robust estimates required specific information on crop management, +15 years of observed daily climate data from weather stations in major crop production zones, and coverage of 40–50% of total national production area. National Yp estimates were weighted by potential production within 100-km of reference weather stations. This protocol is appropriate for countries in which crops are mostly grown in landscapes with relatively homogenous topography, such as prairies, plains, large valleys, deltas and lowlands, which account for a majority of global food crop production. Results are consistent with the hypothesis that average farm yields plateau when they reach 75–85% of estimated national Yp, which appears to occur for rice in China and wheat in Germany. Prediction of when average crop yields will plateau in other countries is now possible based on the estimated Yp or Yw ceiling using this protocol

Creating long-term weather data from thin air for crop simulation modeling

Simulating crop yield and yield variability requires long-term, high-quality daily weather data, including solar radiation, maximum (Tmax) and minimum temperature (Tmin), and precipitation. In many regions, however, daily weather data of sufficient quality and duration are not available. To overcome this limitation, we evaluated a new method to create long-term weather series based on a few years of observed daily temperature data (hereafter called propagated data). The propagated data are comprised of uncorrected gridded solar radiation from the Prediction of Worldwide Energy Resource dataset from the National Aeronautics and Space Administration (NASA–POWER), rainfall from the Tropical Rainfall Measuring Mission (TRMM) dataset, and location-specific calibration of NASA–POWER Tmax and Tmin using a limited amount of observed daily temperature data. The distributions of simulated yields of maize, rice, or wheat with propagated data were compared with simulated yields using observed weather data at 18 sites in North and South America, Europe, Africa, and Asia. Other sources of weather data typically used in crop modeling for locations without long-term observed weather data were also included in the comparison: (i) uncorrected NASA–POWER weather data and (ii) generated weather data using the MarkSim weather generator. Results indicated good agreement between yields simulated with propagated weather data and yields simulated using observed weather data. For example, the distribution of simulated yields using propagated data was within 10% of the simulated yields using observed data at 78% of locations and degree of yield stability (quantified by coefficient of variation) was very similar at 89% of locations. In contrast, simulated yields based entirely on uncorrected NASA–POWER data or generated weather data using MarkSim were within 10% of yields simulated using observed data in only 44 and 33% of cases, respectively, and the bias was not consistent across locations and crops. We conclude that, for most locations, 3 years of observed daily Tmax and Tmin data would allow creation of a robust weather data set for simulation of long-term mean yield and yield stability of major cereal crops