Field‐level crop choice responses to weather‐induced yield shocks in the US Corn Belt

Early and accurate crop yield predictions and prices are crucial for food security management and planning. However, the lack of pre-harvest data poses significant challenges, undermining the reliability and effectiveness of existing methods.This study introduces an innovative approach that addresses these challenges using satellite data products—specifically, Gross Primary Production (GPP) (0.05° spatial resolution) and dimension-reduction techniques to forecast corn yield and price variation across various regions. We predict national corn yield and price variations by leveraging these satellite-derived products. The value of the approach is demonstrated in three case studies conducted for corn in the US (Corn Belt region), Malawi, and South Africa.The predictors are derived from GPP year-on-year variation of each region at the peak growing season, i.e., in July for the US Corn Belt (harvest in October) and March for Malawi and South Africa (harvest in May).We compute the spatial average and Principal Components (PCs) of the GPP year-on-year variations through Empirical Orthogonal Function (EOF) analysis. Additionally, we explore neural network architectures, including Autoencoder (AE) and Variational Autoencoders (VAEs), and extract latent features to reduce the dimension of the GPP data from several thousand to a dozen synthetic features. The PCs, the AE and VAE latent features are used as predictors in Generalized Linear Models (GLM) and Least Absolute Shrinkage and Selection Operator (LASSO) models for predicting year-to-year corn yield and price variation. A neural network is also trained to predict yield and price variations from the latent features for comparison. All models are evaluated using year-to-year cross-validation with three metrics, i.e., Area Under Curve (AUC), the Brier Skill Score (BSS), and the Matthew Correlation Coefficient (MCC). Our results demonstrate the superior predictive performance of PCs for US corn yield variations with an AUC of 0.97 (95% CI 0.92-1), a BSS of 0.75, and an MCC of 0.83.This approach outperforms alternative methods in performance, simplicity, and execution speed. The EOF approach also yields superior results for yield variation prediction in South Africa with an AUC of 0.88 (95% CI 0.75-0.99), a BSS of 0.47, and an MCC of 0.61, while the autoencoder approach is most effective for Malawi with an AUC of 0.98 (95% CI 0.93-1), a BSS of 0.75 and an MCC of 0.83.For price, our results indicate that the spatial averages of GPP year-on-year July variation in the US Corn Belt can be used to forecast the forthcoming increase or decrease in global corn price at harvest with an AUC of 0.92 (95% CI 0.75-0.99), a BSS of 0.5 and an MCC of 0.66. However, in South Africa and Malawi, the most accurate price predictions are obtained with the VAE approach. With VAE, the AUC is 0.75 (95% CI 0.59-0.92), the BSS is 0.2, and the MCC is 0.27 in South Africa, while these metrics reach 0.94 (95% CI 0.59-0.92), 0.63, and 0.7 in Malawi.This study highlights the value of combining satellite data with dimension-reduction methods for large-scale prediction of crop yields and price variations several months before harvest.

Early forecasting of corn yield and price variations using satellite vegetation products

ABSTRACTMaize (Zea mays L.) is among the most important grains contributing to global food security. Eighty years of genetic gain for yield of maize under both favorable and unfavorable stress‐prone drought conditions have been documented for the US Corn Belt, yet maize remains vulnerable to drought conditions, especially at the critical developmental stage of flowering. Optimum AQUAmax (Dupont Pioneer) maize hybrids were developed for increased grain yield under drought and favorable conditions in the US Corn Belt. Following the initial commercial launch in 2011, a large on‐farm data set has been accumulated (10,731 locations) comparing a large sample of the AQUAmax hybrids (78 hybrids) to a large sample of industry‐leading hybrids (4287 hybrids) used by growers throughout the US Corn Belt. Following 3 yr (2011–2013) of on‐farm industry‐scale testing, the AQUAmax hybrids were on average 6.5% higher yielding under water‐limited conditions (2006 locations) and 1.9% higher yielding under favorable growing conditions (8725 locations). In a complementary study, 3 yr (2010–2012) of hybrid‐by‐management‐by‐environment evaluation under water‐limited conditions (14 locations) indicated that the AQUAmax hybrids had greater yield at higher plant populations when compared to non‐AQUAmax hybrids. The combined results from research (2008–2010) and on‐farm (2011–2013) testing throughout the US Corn Belt over the 6‐yr period from 2008 to 2013 indicate that the AQUAmax hybrids offer farmers greater yield stability under water‐limited conditions with no yield penalty when the water limitations are relieved and growing conditions are favorable.

Crop yield variability is a defining characteristic of agriculture. Variations in yield and production are strongly influenced by fluctuations in weather. Concern has been expressed about the consequences of the buildup of greenhouse gases (GHGs) in the atmosphere on long-term climate patterns, including the frequency of extreme events, and the subsequent effect on crop yields and yield variability. In this chapter we present background on the variability issue, including a review of the physical and human dimensions of climate change as related to agricultural production. We also present the results of two recent studies; the first focuses on the effects of climatic variability on yields and the second on the effects of increases in extreme weather events on agriculture. The first study shows that temperature and precipitation changes affect both the mean and variances of crop yields, usually in opposite ways, e.g. under increasing temperatures, corn yields decrease and yield variance increases, while increases in precipitation increase corn yields and reduce variability. In the second study, increases in the frequency and strength of one type of extreme event, the El Niño-Southern Oscillation or ENSO, results in economica damages to agriculture. These damages can be averted by using forecasts of such events in agricultural planting decisions.

Farmers, industry, governments and environmental groups agree that it would be useful to manage transgenic crops producing insecticidal proteins to delay the evolution of resistance in target pests. The main strategy proposed for delaying resistance to Bacillus thuringiensis ( Bt) toxins in transgenic crops is the high-dose/refuge strategy. This strategy is based on the unverified assumption that resistance alleles are initially rare (<10(-3)). We used an F(2) screen on >1,200 isofemale lines of Ostrinia nubilalis Hübner (Lepidoptera: Crambidae) collected in France and the US corn belt during 1999-2001. In none of the isofemale lines did we detect alleles conferring resistance to Bt maize producing the Cry1Ab toxin. A Bayesian analysis of the data indicates that the frequency of resistance alleles in France was <9.20 x 10(-4) with 95% probability, and a detection probability of >80%. In the northern US corn belt, the frequency of resistance to Bt maize was <4.23 x 10(-4) with 95% probability, and a detection probability of >90%. Only 95 lines have been screened from the southern US corn belt, so these data are still inconclusive. These results suggest that resistance is probably rare enough in France and the northern US corn belt for the high-dose plus refuge strategy to delay resistance to Bt maize.

Life cycle and economic assessment of corn production practices in the western US Corn Belt

Heat stress to jeopardize crop production in the US Corn Belt based on downscaled CMIP5 projections

A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis

The C4 grass Zea mays (maize or corn) is the third most important food crop globally in terms of production and demand is predicted to increase 45% from 1997 to 2020. However, the effects of rising [CO2] upon C4 plants, and Z. mays specifically, are not sufficiently understood to allow accurate predictions of future crop production. A rainfed, field experiment utilizing free‐air concentration enrichment (FACE) technology in the primary area of global corn production (US Corn Belt) was undertaken to determine the effects of elevated [CO2] on corn. FACE technology allows experimental treatments to be imposed upon a complete soil–plant–atmosphere continuum with none of the effects of experimental enclosures on plant microclimate. Crop performance was compared at ambient [CO2] (354 μ mol mol−1) and the elevated [CO2] (549 μmol mol−1) predicted for 2050. Previous laboratory studies suggest that under favorable growing conditions C4 photosynthesis is not typically enhanced by elevated [CO2]. However, stomatal conductance and transpiration are decreased, which can indirectly increase photosynthesis in dry climates. Given the deep soils and relatively high rainfall of the US Corn Belt, it was predicted that photosynthesis would not be enhanced by elevated [CO2]. The diurnal course of gas exchange of upper canopy leaves was measured in situ across the growing season of 2002. Contrary to the prediction, growth at elevated [CO2] significantly increased leaf photosynthetic CO2 uptake rate (A) by up to 41%, and 10% on average. Greater A was associated with greater intercellular [CO2], lower stomatal conductance and lower transpiration. Summer rainfall during 2002 was very close to the 50‐year average for this site, indicating that the year was not atypical or a drought year. The results call for a reassessment of the established view that C4 photosynthesis is insensitive to elevated [CO2] under favorable growing conditions and that the production potential of corn in the US Corn Belt will not be affected by the global rise in [CO2].

Most farmland in the US Corn Belt is used to grow row crops at large scales (e.g., corn, soybean) that are highly processed before entering the human food stream rather than specialty crops grown in smaller areas and meant for direct human consumption (table food). Bolstering local table food production close to urban populations in this region through peri-urban agriculture (PUA) could enhance sustainability and resilience. Understanding factors influencing PUA producers' preferences and willingness to produce table food would enable supportive planning and policy efforts. This study combined land use visualization and survey data to examine the potential for increased local table food production for the US Corn Belt. We developed a spatial visualization of current agricultural land use and a future scenario with increased table food production designed to meet 50% of dietary requirements for a metropolitan population in 2050. A survey was administered to row crop (1360) and specialty crop (55) producers near Des Moines, Iowa, US to understand current and intended agricultural land use and factors influencing production. Responses from 316 row crop and 25 specialty crop producers were eligible for this analysis. A future scenario with increased table food production would require less than 3% of available agricultural land and some additional producers (approximately 130, primarily for grain production). Survey responses indicated PUA producers planned small increases in table food production in the next three to five years. Producer plans, including land rental for table food production, could provide approximately 25% of residents' fruit, vegetables, and grains, an increase from the baseline of 2%. Row crop producers ranked food safety regulations, and specialty producers ranked labor concerns as strong influences on their decision-making. Both groups indicated that crop insurance and processing facilities were also important. Increasing table food production by clustering mid-scale operations to increase economies of scale and strengthening supply chains and production infrastructure could provide new profitable opportunities for farmers and more resilient food systems for growing urban regions in the US Corn Belt. Continuing to address producer factors and landscape-scale environmental impacts will be critical in considering food system sustainability challenges holistically.

2.12 - Food Security Implications of Climate Variability and Climate Change

Long-term croplands water productivity in response to management and climate in the Western US Corn Belt

Abstract. Predicting within-field crop yield early in the season can help address crop production challenges to improve farmers’ economic return. While yield prediction with remote sensing has been a research aim for years, it is only recently that observations with the suited spatial and temporal resolutions have become accessible to improve crop yield predictions.Here we developed a yield prediction framework that integrates daily high-resolution (3 m) CubeSat imagery into the APSIM crop model. The approach trains a regression model that correlates simulated yield to simulated leaf area index (LAI) from APSIM. That relationship is then employed to determine the optimum date at which the regression best predicts yield from the LAI. Additionally, our approach can forecast crop yield by utilizing a particle filter to assimilate CubeSat-based LAI in the model APSIM to generate yield maps at 3 m several weeks before the optimum regression date. Our method was evaluated for a rainfed site located in the US Corn belt, using a collection of spatially varying yield data. The proposed approach does not need in situ data to rain the regression, with outcomes reporting that even with a single assimilation step, accurate yield predictions were provided up to 21 days before the optimum regression date. The spatial variability of crop yield was reproduced fairly well, with a good correlation against in situ measurements (R2 = 0.73 and RMSE = 1.69), demonstrating that high-resolution yield predictions early in the season have great potential to meet and improve upon digital agricultural goals.

The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in São Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol) and a Red Yellow Latossol (Ultisol). Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.

Interest from the US commercial aviation industry and commitments established by the US Navy and Air Force to use renewable fuels has spurred interest in identifying and developing crops for renewable aviation fuel. Concern regarding greenhouse gas emissions associated with land‐use change and shifting land grown for food to feedstock production for fuel has encouraged the concept of intensifying current prominent cropping systems through various double cropping strategies. Camelina (Camelina sativaL.) and field pennycress (Thlaspi arvenseL.) are two winter oilseed crops that could potentially be integrated into the corn (Zea maysL.)–soybean [(Glycine max(L.) Merr.] cropping system, which is the prominent cropping system in the US Corn Belt. In addition to providing a feedstock for renewable aviation fuel production, integrating these crops into corn–soybean cropping systems could also potentially provide a range of ecosystem services. Some of these include soil protection from wind and water erosion, soil organic C (SOC) sequestration, water quality improvement through nitrate reduction, and a food source for pollinators. However, integration of these crops into corn–soybean cropping systems also carries possible limitations, such as potential yield reductions of the subsequent soybean crop. This review identifies and discusses some of the key benefits and constraints of integrating camelina or field pennycress into corn–soybean cropping systems and identifies generalized areas for potential adoption in the US Corn Belt.