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NASA NTRS2020

Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data

Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R(exp 2), RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p < 0.01), and harvesting dates were delayed by 0.78 days/year (p < 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p < 0. 01) or a 0.66-day harvest delay (r = 0.10, p < 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p < 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p < 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.

North America↗

DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.

3D crop modeling↗

DOE OSTI2015

Investment risk in bioenergy crops

Here, perennial, cellulosic bioenergy crops represent a risky investment. The potential for adoption of these crops depends not only on mean net returns, but also on the associated probability distributions and on the risk preferences of farmers. Using 6-year observed crop yield data from highly productive and marginally productive sites in the southern Great Lakes region and assuming risk neutrality, we calculate expected breakeven biomass yields and prices compared to corn ( Zea mays L.) as a benchmark. Next we develop Monte Carlo budget simulations based on stochastic crop prices and yields. The crop yield simulations decompose yield risk into three components: crop establishment survival, time to maturity, and mature yield variability. Results reveal that corn with harvest of grain and 38% of stover (as cellulosic bioenergy feedstock) is both the most profitable and the least risky investment option. It dominates all perennial systems considered across a wide range of farmer risk preferences. Although not currently attractive for profit-oriented farmers who are risk neutral or risk averse, perennial bioenergy crops.

09 BIOMASS FUELS↗

DOE OSTI2017

Consensus, uncertainties and challenges for perennial bioenergy crops and land use

Perennial bioenergy crops have significant potential to reduce greenhouse gas (GHG) emissions and contribute to climate change mitigation by substituting for fossil fuels; yet delivering significant GHG savings will require substantial land-use change, globally. Over the last decade, research has delivered improved understanding of the environmental benefits and risks of this transition to perennial bioenergy crops, addressing concerns that the impacts of land conversion to perennial bioenergy crops could result in increased rather than decreased GHG emissions. For policymakers to assess the most cost-effective and sustainable options for deployment and climate change mitigation, synthesis of these studies is needed to support evidence-based decision making. In 2015, a workshop was convened with researchers, policymakers and industry/business representatives from the UK, EU and internationally. Outcomes from global research on bioenergy land-use change were compared to identify areas of consensus, key uncertainties, and research priorities. Here, we discuss the strength of evidence for and against six consensus statements summarizing the effects of land-use change to perennial bioenergy crops on the cycling of carbon, nitrogen and water, in the context of the whole life-cycle of bioenergy produc-tion. Our analysis suggests that the direct impacts of dedicated perennial bioenergy crops on soil carbon and nitrous oxide are increasingly well understood and are often consistent with significant life cycle GHG mitiga-tion from bioenergy relative to conventional energy sources. We conclude that the GHG balance of perennial bioenergy crop cultivation will often be favourable, with maximum GHG savings achieved where crops are grown on soils with low carbon stocks and conservative nutrient application, accruing additional environmental benefits such as improved water quality. The analysis reported here demonstrates there is a mature and increasingly comprehensive evidence base on the environmental benefits and risks of bioenergy cultivation which can support the development of a sustainable bioenergy industry.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Assessment of potential greenhouse gas mitigation from changes to crop root mass and architecture

Reducing (and eventually reversing) the increase in greenhouse gases (GHGs) in the atmosphere due to human activities, and thus reducing the extent and severity of anthropogenic climate change, is one of the great challenges facing humanity. While most of the man-caused increase in GHGs has been due to fossil fuel use, land use (including agriculture) currently accounts for about 25% of total GHG emissions and thus there is a need to include emission reductions from the land use sector as part of an effective climate change mitigation strategy. In addition, analyses included in the recent IPCC 5th Climate Change Assessment report suggests that it may not be possible to achieve large enough emissions reductions in the energy, transport and industrial sectors alone to stabilize GHG concentrations at a level commensurate with a less than 2°C global average temperature increase, without the help of a substantial CO 2 sink (i.e., atmospheric CO 2 removal) from the land use sector. One of the potential carbon sinks that could contribute to this goal is increasing C storage in soil organic matter on managed lands. This report details a preliminary scoping analysis, to assess the potential agricultural area in the US – where appropriate soil, climate and land use conditions exist – to determine the land area on which ‘improved root phenotype’ crops could be deployed and to evaluate the potential long-term soil C storage, given a set of ‘bounding scenarios’ of increased crop root input and/or rooting depth for major crop species (e.g., row crops (corn, sorghum, soybeans), small grains (wheat, barley, oats), and hay and pasture perennial forages). The enhanced root phenotype scenarios assumed 25, 50 and 100% increase in total root C inputs, in combination with five levels of modifying crop root distributions (i.e., no change and four scenarios with increasing downward shift in root distributions). We also analyzed impacts of greater root production on the soil-crop nitrogen balance, from the standpoint of increased need for additional N inputs and consequences for increased N 2 O flux, as well as potential impacts if more and deeper roots contributed to reduced N leaching. In the enhanced root phenotype scenarios, the implicit assumption was that increases in overall plant production could be achieved (e.g., through increased CO 2 assimilation, greater growth efficiency) without reducing the harvested yield – that is, we did not include potential leakage and land substitution effects from potential decreased crop yield in the analysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2011

Regional Uptake and Release of Crop Carbon in the United States

Carbon fixed by agricultural crops in the US creates regional CO2 sinks where it is harvested and regional CO2 sources where it is released back to the atmosphere. The quantity and location of these fluxes differ depending on the annual supply and demand of crop commodities. Data on the harvest of crop biomass, storage, import and export, and on the use of biomass for food, feed, fiber, and fuel were compiled to estimate an annual crop carbon budget for 2000 to 2008. Net sources of CO2 associated with the consumption of crop commodities occurred in the Eastern Uplands, Southern Seaboard, and Fruitful Rim regions. Net sinks associated with the production of crop commodities occurred in the Heartland, Northern Crescent, Northern Great Plains, and Mississippi Portal regions. The national crop carbon budget was balanced to within 0.7 to 6.6% yr-1 during the period of this analysis.

09 BIOMASS FUELS↗

NASA NTRS2016

Satellite Estimation of Fractional Cover in Several California Specialty Crops

Past research in California and elsewhere has revealed strong relationships between satellite NDVI, photosynthetically active vegetation fraction (Fc), and crop evapotranspiration (ETc). Estimation of ETc can support efficiency of irrigation practice, which enhances water security and may mitigate nitrate leaching. The U.C. Cooperative Extension previously developed the CropManage (CM) web application for evaluation of crop water requirement and irrigation scheduling for several high-value specialty crops. CM currently uses empirical equations to predict daily Fc as a function of crop type, planting date and expected harvest date. The Fc prediction is transformed to fraction of reference ET and combined with reference data from the California Irrigation Management Information System to estimate daily ETc. In the current study, atmospherically-corrected Landsat NDVI data were compared with in-situ Fc estimates on several crops in the Salinas Valley during 2011-2014. The satellite data were observed on day of ground collection or were linearly interpolated across no more than an 8-day revisit period. Results will be presented for lettuce, spinach, celery, broccoli, cauliflower, cabbage, peppers, and strawberry. An application programming interface (API) allows CM and other clients to automatically retrieve NDVI and associated data from NASA's Satellite Irrigation Management Support (SIMS) web service. The SIMS API allows for queries both by individual points or user-defined polygons, and provides data for individual days or annual timeseries. Updates to the CM web app will convert these NDVI data to Fc on a crop-specific basis. The satellite observations are expected to play a support role in Salinas Valley, and may eventually serve as a primary data source as CM is extended to crop systems or regions where Fc is less predictable.

satellite↗

NASA NTRS2022

Compound Heat and Moisture Extreme Impacts on Global Crop Yields Under Climate Change

Extreme heat, drought and moisture excess are increasingly co-occurring within a single growing season, impacting crop yields in global breadbasket regions. In this Review, we synthesize understanding of compound heat and moisture extremes, their impacts on global crop yields and implications for adaptation. Heat and moisture extremes and their impacts become compounded through crop-physiological interactions, heat–moisture couplings in the climate system and crop–atmosphere interactions. Since around 2000, these compound extremes, and hot droughts in particular, have been linked to especially poor harvests (up to 30% yield losses) in regions such as India, Ethiopia, the USA, Europe and Russia. However, in some cases, combinations of crop stresses might generate compensating effects. Compound extremes are projected to increase in frequency and amplitude in the future, but, owing to the biophysical interdependence among temperature, water and crop physiology, the net yield effects of such future compound extremes remain uncertain. Accordingly, compound extremes will necessitate comprehensive agricultural adaptation strategies geared towards multi-stress resilience, as adaptations that work for single climate stresses could be maladaptive under combined stresses. An integrated understanding of heat and water in soil–plant–atmosphere dynamics is urgently needed to understand risks and suitably adapt cropping systems to compounding climate impacts.

Agroecology↗

NASA NTRSDate not supplied

Plants in Space and Space Crop Production

As astronauts venture farther from Earth, and for longer periods, the space food system will increase in importance. Crop production can supplement a packaged diet to provide additional nutrients and dietary variety for astronauts, and in future missions, bioregenerative approaches may be used to generate a larger percentage of the diet. Plants may also provide behavioural health benefits and assist with other life support functions. Several unique challenges exist for growth of plants in microgravity and on other planetary surfaces like the moon and Mars. Testing with the Veggie and Advanced Plant Habitat (APH) chambers on the International Space Station is allowing us to understand the impacts of gravity and spaceflight on crop growth, nutritional content, acceptability, and the importance of plants to astronauts living and working away from Earth. We are also gaining a better understanding of food safety concerns and the behaviour of space plant microbiomes and plant pathogens. As we transition from research towards operational space crop production to enable human exploration, there are several gaps and challenges of growing crops in space that must be addressed. Research and technology development in key areas such as water and nutrient delivery, plant health monitoring, and crop selection are needed to overcome these challenges. Additionally, there are opportunities for breeding or engineering of custom space crops related to plant growth and development, plant physiology, produce nutrition, organoleptic acceptability, and post-harvest characteristics. Solutions to help ensure food security off-Earth may also translate to better approaches to terrestrial sustainable crop production.

Advanced Plant Habitat↗

NASA NTRS2023

Plants in Space and Space Crop Production

Advanced Plant Habitat↗

DOE OSTI2021

Root water uptake of biofuel crops revealed by coupled electrical resistivity and soil water content measurements

Abstract Biofuel crops, including annuals such as maize ( Zea mays L.), soybean [ Glycine max (L.) Merr.], and canola ( Brassica napus L.), as well as high‐biomass perennial grasses such as miscanthus ( Miscanthus × giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz), are candidates for sustainable alternative energy sources. However, large‐scale conversion of croplands to perennial biofuel crops could have substantial impacts on regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales, these differences in crop water use are not well known. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. In this study, we demonstrate the use of a novel coupled hydrogeophysical approach with both time domain reflectometry soil water content and ER measurements to compare root water uptake and soil properties of an annual crop rotation with the perennial grass miscanthus, across three growing seasons (2009–2011) in southwest Michigan, USA. We estimated maximum root depths to be between 1.2 and 2.2 m, with the vertical distribution of roots being notably deeper in 2009 relative to 2010 and 2011, likely due to the drought conditions during that first year. Modeled cumulative ET of both crops was underestimated (2–34%) relative to estimates obtained from soil water drawdown in prior studies but was found to be greater in the perennial grass than the annual crops, despite shallower modeled rooting depths in 2010 and 2011.

Kuhl, Alexandria S.↗

DOE OSTI2019

Quantity and quality of water percolating below the root zone of three biofuel feedstock crop systems

Biofuel demand is on the rise, yet there is limited information available about the likely implications of increased planting of cellulosic biofuel crops on water and nutrient fluxes. To help address this gap, this study quantifies subsurface water, nitrate, and dissolved phosphorus fluxes below feedstock crops managed for biofuel production on naturally well drained soils in southern Wisconsin. Automated equilibrium tension lysimeters were used to measure subsurface (below root zone) fluxes of water and nutrients below continuous corn (CC), monoculture switchgrass (SG), and hybrid poplar (HP) cropping systems for three years. Average water fluxes below SG were nearly double those below CC and HP. Most of this difference can be linked to seasonal events, particularly during late winter and early spring when subsurface water fluxes and increased soil moisture were observed below SG plots, likely due to enhanced macropore flow. Nitrate fluxes were approximately 14 times higher below CC than the 37.4 kg NO 3 -N/ha three year annual average observed for SG; the nitrate fluxes were negligible below HP despite similar nitrogen application rates. The high flux in one of the two CC plots is likely due to excessive manure applications prior to the study period; however, the nitrate loss potential from CC is much higher than the perennial crops. Dissolved reactive phosphorus (DRP) concentrations were very low below all three cropping systems (average of 0.076, 0.083, 0.034 mg/L in SG, CC, and HP respectively). For the cropping systems evaluated, nitrate fluxes were driven by concentration differences in leachate and not influenced by nominal differences in N fertilizer inputs, whereas DRP fluxes were mainly driven by water fluxes and therefore independent of phosphorus inputs and soil test phosphorus. The data in this paper provide a foundation to better understand differences in fluxes below different biofuel cropping systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Estimating crop net primary production using inventory data and MODIS-derived parameters

National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale and over national and continental extents. Existing satellite-based NPP products tend to underestimate NPP on croplands. A new Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP. The method is documented here and evaluated for corn and soybean crops in Iowa and Illinois in years 2006 and 2007. The method includes a crop-specific enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), shortwave radiation data estimated using Mountain Climate Simulator (MTCLIM) algorithm and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that correspond to the Cropland Data Layer (CDL) land cover product. The modeling framework represented well the gradient of NPP across Iowa and Illinois, and also well represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 980 g C m-2 yr-1 and 420 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Estimated gross primary productivity (GPP) derived from AgI-LUE were in close agreement with eddy flux tower estimates. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.

Bandaru, Varaprasad↗

DOE OSTI2026

Economic Incentives for Agrivoltaics Systems with Commodity Crops in the Midwestern United States

Declining costs of photovoltaic (PV) technology and rising market and policy incentives are leading to the growing deployment of PV on cropland in the US Midwest, leading to concerns about the displacement of food and feed crop production. Agrivoltaic (AV) technology enables the dual use of land by co-locating PV energy and crop production, potentially reducing land-use competition with crop production. We develop a benefit-cost analysis framework to compare the net economic returns from AV to those with stand-alone PV and crop production on a representative field and show conditions under which AV can be more profitable for both a solar developer and a farmer. We integrate it with a crop and solar energy model to simulate the performance of various field designs and space and height configurations in AV systems to accommodate soybean production with conventional farm equipment under representative conditions in the US Midwest. We find that an AV system with soybean production is less profitable than PV alone for a solar developer due to the high capital costs of raising panel height, and less profitable for a farmer than leasing land for PV due to its adverse effects of shading on crop yield. We discuss the changes in technology and market prices of solar energy and soybeans that are necessary to make the AV system profitable for solar developers and farmers. We show that AV can worsen rather than mitigate the conflict between food crops and solar energy production in the Midwest.

14 SOLAR ENERGY↗

DOE OSTI2015

Impacts of elevated atmospheric CO2 on nutrient content of important food crops

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO 2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO 2 enrichment (FACE) technology to expose crops to ambient and elevated CO 2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO 2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients.

Science & Technology - Other Topics↗

DOE OSTI2021

Heat stress on agricultural workers exacerbates crop impacts of climate change

Abstract The direct impacts of climate change on crop yields and human health are individually well-studied, but the interaction between the two have received little attention. Here we analyze the consequences of global warming for agricultural workers and the crops they cultivate using a global economic model (GTAP) with explicit treatment of the physiological impacts of heat stress on humans’ ability to work. Based on two metrics of heat stress and two labor functions, combined with a meta-analysis of crop yields, we provide an analysis of climate, impacts both on agricultural labor force, as well as on staple crop yields, thereby accounting for the interacting effect of climate change on both land and labor. Here we analyze the two sets of impacts on staple crops, while also expanding the labor impacts to highlight the potential importance on non-staple crops. We find, worldwide, labor and yield impacts within staple grains are equally important at +3 ∘ C warming, relative to the 1986–2005 baseline. Furthermore, the widely overlooked labor impacts are dominant in two of the most vulnerable regions: sub-Saharan Africa and Southeast Asia. In those regions, heat stress with 3 ∘ C global warming could reduce labor capacity in agriculture by 30%–50%, increasing food prices and requiring much higher levels of employment in the farm sector. The global welfare loss at this level of warming could reach $136 billion, with crop prices rising by 5%, relative to baseline.

de Lima, Cicero Z. (ORCID:0000000297005435)↗