skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Reconsidering the Lessons Learned from the 1970 Southern Corn Leaf Blight Epidemic
The southern corn leaf blight epidemic of 1970 caused estimated losses of about 16% for the U.S. corn crop, equivalent to about $8 billion in current terms. The epidemic was caused by the prevalence of Texas male sterile cytoplasm ( cms-T), used to produce most of the hybrid corn seed planted that year, combined with the emergence of a novel race of the fungus Cochliobolus heterostrophus that was exquisitely virulent on cms-T corn. Remarkably, the epidemic lasted just a single year. This episode has often been portrayed in the literature and textbooks over the last 50 years as a catastrophic mistake perpetrated by corn breeders and seed companies of the time who did not understand or account for the dangers of crop genetic uniformity. In this perspective article, we aim to present an alternative interpretation of these events. First, we contend that, rather than being caused by a grievous error on the part of the corn breeding and seed industry, this epidemic was a particularly unfortunate, unusual, and unlucky consequence of a technological advancement intended to improve the efficiency of corn seed production for America's farmers. Second, we tell the story of the resolution of the epidemic as an example of timely, meticulously applied research in the public sector for the public good.  more » « less
Award ID(s):
2154872
PAR ID:
10598124
Author(s) / Creator(s):
;
Publisher / Repository:
American Phytopathological Society
Date Published:
Journal Name:
Phytopathology®
Volume:
114
Issue:
9
ISSN:
0031-949X
Page Range / eLocation ID:
2007 to 2016
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 and 0.07 days/year, respectively. Corn harvesting dates were also delayed at a rate of 0.67 days/year, while advanced soybean harvesting occurred at a rate of 0.05 days/year. The growing season length has increased considerably at a rate of 0.66 days/year for corn and was shortened by 0.12 days/year for soybean. Sensitivity analysis showed that planting dates were more sensitive to the early season temperature, while harvesting dates were significantly correlated with temperature over the entire growing season. In terms of the changing climatic factors, only the increased summer precipitation was statistically related to the delayed corn harvesting dates in Kentucky. Further analysis showed that the increased corn yield was significantly correlated with the delayed harvesting dates (1.37 Bu/acre per day) and extended growing season length (1.67 Bu/acre per day). Our results suggested that seasonal climate change (e.g., summer precipitation) was the main factor influencing crop phenological trends, particularly corn harvesting in Kentucky over the study period. We also highlighted the critical role of changing crop phenology in constraining crop production, which needs further efforts for optimizing crop management practices. 
    more » « less
  2. Abstract With climate change threatening agricultural productivity and global food demand increasing, it is important to better understand which farm management practices will maximize crop yields in various climatic conditions. To assess the effectiveness of agricultural practices, researchers often turn to randomized field experiments, which are reliable for identifying causal effects but are often limited in scope and therefore lack external validity. Recently, researchers have also leveraged large observational datasets from satellites and other sources, which can lead to conclusions biased by confounding variables or systematic measurement errors. Because experimental and observational datasets have complementary strengths, in this paper we propose a method that uses a combination of experimental and observational data in the same analysis. As a case study, we focus on the causal effect of crop rotation on corn (maize) and soybean yields in the Midwestern United States. We find that, in terms of root mean squared error, our hybrid method performs 13% better than using experimental data alone and 26% better than using the observational data alone in the task of predicting the effect of rotation on corn yield at held-out experimental sites. Further, the causal estimates based on our method suggest that benefits of crop rotations on corn yield are lower in years and locations with high temperatures whereas the benefits of crop rotations on soybean yield are higher in years and locations with high temperatures. In particular, we estimated that the benefit of rotation on corn yields (and soybean yields) was 0.85 t ha−1(0.24 t ha−1) on average for the top quintile of temperatures, 1.03 t ha−1(0.21 t ha−1) on average for the whole dataset, and 1.19 t ha−1(0.16 t ha−1) on average for the bottom quintile of temperatures. This association between temperatures and rotation benefits is consistent with the hypothesis that the benefit of the corn-soybean rotation on soybean yield is largely driven by pest pressure reductions while the benefit of the corn-soybean rotation on corn yields is largely driven by nitrogen availability. 
    more » « less
  3. null (Ed.)
    Fluctuations in temperature and precipitation are expected to increase with global climate change, with more frequent, more intense and longer-lasting extreme events, posing greater challenges for the security of global food production. Here we proposed a generic framework to assess the impact of climate-induced crop yield risk under both current and future scenarios by combining a stochastic model for synthetic climate generation with a well-validated statistical crop yield model. The synthetic climate patterns were generated using the extended Empirical Orthogonal Function method based on historically observed and projected climate conditions. We applied our framework to assess the corn and soybean yield risk in the U.S. Midwest for historical and future climate conditions. We found that: (1) in the U.S. Midwest, about 45% and 40% of the interannual variability in corn and soybean yield, respectively, can be explained by the climate; (2) the risk level is higher in the southwest and northwest regions of the U.S. Midwest corresponding to 25% yield reduction for both corn and soybean compared to other regions; (3) the severity for the 1988 and 2012 major droughts quantified by our method represent 21-year and 30-year events for corn, and 7-year and 12-year events for soybean, respectively; (4) the crop yield risk will increase under a future climate scenario (i.e., Representative Concentration Pathway 8.5 or RCP 8.5 at 2050) compared with the current climate condition, with averaged yield decreases and yield variability increases for both corn and soybean. The framework and the results of this study enable applications for risk management policies and practices for the agriculture sectors. 
    more » « less
  4. Plant counting is a critical aspect of crop management, providing farmers with valuable insights into seed germination success and within-field variation in crop population density, both of which are key indicators of crop yield and quality. Recent advancements in Unmanned Aerial System (UAS) technology, coupled with deep learning techniques, have facilitated the development of automated plant counting methods. Various computer vision models based on UAS images are available for detecting and classifying crop plants. However, their accuracy relies largely on the availability of substantial manually labeled training datasets. The objective of this study was to develop a robust corn counting model by developing and integrating an automatic image annotation framework. This study used high-spatial-resolution images collected with a DJI Mavic Pro 2 at the V2–V4 growth stage of corn plants from a field in Wooster, Ohio. The automated image annotation process involved extracting corn rows and applying image enhancement techniques to automatically annotate images as either corn or non-corn, resulting in 80% accuracy in identifying corn plants. The accuracy of corn stand identification was further improved by training four deep learning (DL) models, including InceptionV3, VGG16, VGG19, and Vision Transformer (ViT), with annotated images across various datasets. Notably, VGG16 outperformed the other three models, achieving an F1 score of 0.955. When the corn counts were compared to ground truth data across five test regions, VGG achieved an R2 of 0.94 and an RMSE of 9.95. The integration of an automated image annotation process into the training of the DL models provided notable benefits in terms of model scaling and consistency. The developed framework can efficiently manage large-scale data generation, streamlining the process for the rapid development and deployment of corn counting DL models. 
    more » « less
  5. Abstract The transition from conventional to more regenerative cropping systems can be economically risky due to variable transition period yields and unforeseen costs. We compared yields and economic returns for the first 3 years of the transition from a business as usual (BAU) conventional corn (Zea mays)–soybean (Glycine max) rotation to an aspirational (ASP) five‐crop (corn‐soybean‐winter wheat [Triticum aestivum]–winter canola [Brassica napus]‐forage) rotation in the Upper Midwest United States. Regenerative ASP cropping practices included the more diverse crop rotation, continuous no‐till, cover crops, precision inputs, and livestock (compost) integration. For the first two transition years, BAU corn yields were 8%–12% higher than ASP while in the third transition year, BAU corn yields were 5% lower. Soybean yields were similar for the first 2 years but higher in BAU in the third year due to an ASP pest outbreak. Equivalent yields for other ASP crops were lower than BAU in the first 2 years but similar in the third year except for canola, which suffered from slug damage. Whole‐system economic returns narrowed across years; by year three, whole system comparisons for the ASP corn and soybean entry points (corn‐soybean‐wheat and soybean‐wheat‐canola, respectively) showed equivalent economic returns for BAU and ASP, despite yield differences, owing largely to the ASP system's reduced operational costs. Overall findings suggest that early regenerative systems can be as profitable as conventional systems with careful attention to rotation entry points and inputs. 
    more » « less