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: Divergent changes in crop yield loss risk due to droughts over time in the US
Abstract Drought poses a major threat to agricultural production and food security. This study evaluates the changes in drought-induced crop yield loss risk for six crops (alfalfa, barley, corn, soybean, spring wheat, and winter wheat) between 1971–2000 and 1991–2020 across the contiguous US. Using a copula-based probabilistic framework, our results reveal a spatially heterogeneous change in yield risk to meteorological droughts, which varies with crop types. Regional analyses identify the largest temporal decline in yield risk in the Southeast and Upper Midwest, while the Northwest and South show an increase in risk. Among the considered anthropogenic and climatic drivers of crop productivity, changes in climatic variables such as high temperatures (e.g., killing degree days), vapor pressure deficit and precipitation show significantly stronger associations with changes in yield risk than irrigated area and nitrogen fertilizer application. Among the counties that observe drier drought events, only 55% exhibit an increase in crop yield loss risk due to drier droughts. The rest 45% show a decrease in yield loss risk due to mediation of favorable climatic and anthropogenic factors. Alarmingly, more than half (for barley and spring wheat), and one-third (for alfalfa, corn, soybean and winter wheat) of that the risk increasing regions have outsized influence on destabilizing national crop production. The findings provide valuable insights for policymakers, agricultural stakeholders, and decision-makers in terms of the potential ways and locations to be prioritized for enhancing local and national agricultural resilience and ensuring food security.  more » « less
Award ID(s):
2317819 2019561 1856054
PAR ID:
10543178
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IOP Publishing
Date Published:
Journal Name:
Environmental Research Letters
Volume:
19
Issue:
11
ISSN:
1748-9326
Format(s):
Medium: X Size: Article No. 114008
Size(s):
Article No. 114008
Sponsoring Org:
National Science Foundation
More Like this
  1. Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980–2012, including the Millennium Drought in Australia (2001–2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25–45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops. 
    more » « less
  2. 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
  3. Abstract. Understanding and assessing the spatiotemporal patterns in crop-specific phosphorus (P) fertilizer management are crucial for enhancing crop yield and mitigating environmental problems. The existing P fertilizer dataset, derived from sales data, depicts an average application rate over total cropland at the county level but overlooks cross-crop variations. Conversely, the survey-based dataset offers crop-specific application details at the state level yet lacks inter-state variability. By reconciling these two datasets, we developed long-term gridded maps to characterize crop-specific P fertilizer application rates, timing, and methods across the contiguous US at a resolution of 4 km × 4 km from 1850 to 2022. We found that P fertilizer application rate over fertilized areas in the US increased from 0.9 g P m−2 yr−1 in 1940 to 1.9 g P m−2 yr−1 in 2022, with substantial variations among crops. However, approximately 40 % of cropland nationwide has remained unfertilized in the recent decade. The hotspots for P fertilizer use have shifted from the southeastern and eastern US to the Midwest and the Great Plains over the past century, reflecting changes in cropland area, crop choices, and P fertilizer use across different crops. Pre-planting (fall and spring) and broadcast application are prevalent among corn, soybean, and cotton in the Midwest and the Southeast, indicating a high P loss risk in these regions. In contrast, wheat and barley in the Great Plains receive the most intensive P fertilization at planting and via non-broadcast application. The P fertilizer management dataset developed in this study can advance our comprehension of agricultural P budgets and facilitate the refinement of best P fertilizer management practices to optimize crop yield and to reduce P loss. Datasets are available at https://doi.org/10.5281/zenodo.10700821 (Cao et al., 2024). 
    more » « less
  4. Large-scale modes of climate variability can force widespread crop yield anomalies and are therefore often presented as a risk to food security. We quantify how modes of climate variability contribute to crop production variance. We find that the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), tropical Atlantic variability (TAV), and the North Atlantic Oscillation (NAO) together account for 18, 7, and 6% of globally aggregated maize, soybean, and wheat production variability, respectively. The lower fractions of global-scale soybean and wheat production variability result from substantial but offsetting climate-forced production anomalies. All climate modes are important in at least one region studied. In 1983, ENSO, the only mode capable of forcing globally synchronous crop failures, was responsible for the largest synchronous crop failure in the modern historical record. Our results provide the basis for monitoring, and potentially predicting, simultaneous crop failures. 
    more » « less
  5. null (Ed.)
    Accurate monitoring of crop condition is critical to detect anomalies that may threaten the economic viability of agriculture and to understand how crops respond to climatic variability. Retrievals of soil moisture and vegetation information from satellite-based remote-sensing products offer an opportunity for continuous and affordable crop condition monitoring. This study compared weekly anomalies in accumulated gross primary production (GPP) from the SMAP Level-4 Carbon (L4C) product to anomalies calculated from a state-scale weekly crop condition index (CCI) and also to crop yield anomalies calculated from county-level yield data reported at the end of the season. We focused on barley, spring wheat, corn, and soybeans cultivated in the continental United States from 2000 to 2018. We found that consistencies between SMAP L4C GPP anomalies and both crop condition and yield anomalies increased as crops developed from the emergence stage (r: 0.4–0.7) and matured (r: 0.6–0.9) and that the agreement was better in drier regions (r: 0.4–0.9) than in wetter regions (r: −0.8–0.4). The L4C provides weekly GPP estimates at a 1-km scale, permitting the evaluation and tracking of anomalies in crop status at higher spatial detail than metrics based on the state-level CCI or county-level crop yields. We demonstrate that the L4C GPP product can be used operationally to monitor crop condition with the potential to become an important tool to inform decision-making and research. 
    more » « less