SEARCH · Search NASA

Results for “crop yield simulations”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Adaptation Strategies Strongly Reduce the Future Impacts of Climate Change on Simulated Crop Yields

Abstract Simulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta‐model based on 8703 site‐level process‐model simulations of yield with different future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to predict the percentage change in projected future yield relative to the baseline period (2000–2010) as a function of explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the projected yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6%–21%. Adaptation alleviates this average projected loss by 1–13 percentage points. Maize was most responsive to adaptive practices with a projected mean yield loss of −21% [range across locations: −63%, +3.7%] without adaptation and −7.5% [range: −46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types predicted to most prevent large yield losses, respectively. When adaptation practices are applied, some areas are predicted to experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Spatial Sampling of Weather Data for Regional Crop Yield Simulations

Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.

upscaling↗

NASA NTRS2017

Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

wheat↗

NASA NTRS2015

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.

climate↗

NASA NTRS2016

Spatial and Temporal Uncertainty of Crop Yield Aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Inter-comparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four datasets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia).Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05∗Corresponding (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.

Aggregation uncertainty↗

DOE OSTI2022

CO 2 fertilization of crops offsets yield losses due to future surface ozone damage and climate change

Tropospheric ozone (O 3 ) is harmful to plant productivity and negatively impacts crop yields. O3 concentrations are projected to decrease globally in the optimistic Representative Concentration Pathway of 2.6 W m –2 (RCP2.6) but increase globally following the high-emission scenario under the RCP8.5, with substantial implications for global food security. The damaging effect of O 3 on future crop yield is affected by CO 2 fertilization and climate change, and their interactions for RCP scenarios have yet to be quantified. In this study, we used the Joint UK Land Environment Simulator modified to include crops (JULES-crop) to quantify the impacts, and relative importance of present-day and future O3, CO 2 concentration and meteorology on crop production at the regional scale until 2100 following RCP2.6 and RCP8.5 scenarios. We focus on eight major crop-producing regions that cover the production of wheat, soybean, maize, and rice. Our results show that CO 2 alone has the largest effect on regional yields, followed by climate and O 3 . However, the CO 2 fertilization effect is offset by the negative impact of tropospheric O 3 in regions with high O 3 concentrations, such as South Asia and China. Simulated crop yields in 2050 were compared with Food and Agriculture Organisation (FAO) statistics to investigate the differences between a socioeconomic and a biophysical process-based approach. Results showed that FAO estimates are closer to our JULES-crop RCP8.5 scenario. This study demonstrates that air pollution could be the biggest threat to future food production and highlights an urgent policy need to mitigate the threat of climate change and O 3 pollution on food security.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

AgMIP Regional Integrated Assessment of Agricultural Systems in Nioro, Senegal: Representative Agricultural Pathways, Climate, Crop and Economic Datasets

This paper describes the datasets that were used to implement an AgMIP Regional Integrated Assessment for the Nioro region of Senegal to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin and to assess adaptations of that system to climate change under current as well as future climate and socio-economic conditions. This dataset includes the Representative Agricultural Pathways developed for Nioro from 2000-2050; the climate data that were used to implement crop yield simulations; the data that were used to parameterize the DSSAT and APSIM crop models, including historical climate data and future climate scenarios; and the data that were used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOAMD) economic simulation model, as well as simulated model outputs.

AgMIP↗

NASA NTRS2024

Evaluation of a Combined Drought Indicator Against Crop Yield Estimations and Simulations Over the Argentine Humid Pampas

Droughts pose serious threats to the agricultural sector, especially in rainfed-dominated agricultural regions like those in Argentina’s Humid Pampas. This region was recently impacted by slow-evolving and long-lasting droughts as well as by flash droughts, resulting in losses reaching thousands of millions of US dollars. Improvements of drought early warning systems are essential, particularly given the projected increase in drought frequency and severity over southern South America. The spatial and temporal relationship between precipitation deficits, soil moisture and vegetation health anomalies are crucial for better understanding and representation of the agricultural droughts and their impacts. In this context, the Combined Drought Indicator (CDI) considers the causal and time-lagged relationship of these three variables. The study’s objective is twofold: (1) Analyze the time-lagged response between precipitation deficits, soil moisture and satellite fAPAR anomalies; and (2) Evaluate the CDI’s capability to characterize the severity of drought events on the Humid Pampas against agricultural yield estimations and simulations, as well as agricultural emergency declarations. The correlation among the variables shows strong spatial variability. The highest Pearson correlation values (r > 0.42) are observed over parts of the Humid Pampas for time lags of 0, 10, and 20 days between the variables. Although the CDI has limitations, such as its coarse spatial resolution and monthly temporal resolution of precipitation data, it effectively tracks the progression of major drought events in the region. The CDI’s performance aligns well with estimations and simulations of soybean and corn yields, as well as official declarations of agricultural emergencies. Insights from this study also provide a basis for discussing potential improvements to the CDI. This study highlights the global and regional significance of evaluating and enhancing the CDI for effective drought monitoring, emphasizing the role of collaborative efforts for future advancements in drought early warning systems.

Combined Drought Indicator↗

DOE OSTI2023

Tropical Tree Crop Simulation with a Process-Based, Daily Timestep Simulation Model (ALMANAC): Description of Model Adaptation and Examples with Coffee and Cocoa Simulations

Coffee (Coffea species) and Cocoa (Theobroma cacao) are important cash crops grown in the tropics but traded globally. This study was conducted to apply the ALMANAC model to these crops for the first time, and to test its ability to simulate them under agroforestry management schemes and varying precipitation amounts. To create this simulation, coffee was grown on a site in Kaua’i, Hawai’i, USA, and cocoa was grown on a site in Sefwi Bekwai, Ghana. A stand-in for a tropical overstory tree was created for agroforestry simulations using altered parameters for carob, a common taller tropical tree for these regions. For both crops, ALMANAC was able to realistically simulate yields when compared to the collected total yield data. On Kaua’i, the mean simulated yield was 2% different from the mean measured yield, and in all three years, the simulated values were within 10% of the measured values. For cocoa, the mean simulated yield was 3% different from the mean measured yield and the simulated yield was within 10% of measured yields for all four available years. When precipitation patterns were altered, in Ghana, the wetter site showed lower percent changes in yield than the drier site in Hawai’i. When agroforestry-style management was simulated, a low Leaf Area Index (LAI) of the overstory showed positive or no effect on yields, but when LAI climbed too high, the simulation was able to show the detrimental effect this competition had on crop yields. These simulation results are supported by other literature documenting the effects of agroforestry on tropical crops. This research has applied ALMANAC to new crops and demonstrated its simulation of different management and environmental conditions. The results show promise for ALMANAC’s applicability to these scenarios as well as its potential to be further tested and utilized in new circumstances.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Predicting Field Efficiency of Round-Baling Operations in High-Yielding Biomass Crops

Model simulations for bioenergy harvest planning need to utilize equipment-capacity relationships for equipment operating under the high-yield conditions typical of a biomass crop. These performance assumptions have a direct bearing on the estimates of machine capacity, the number of machines required, and, therefore, the cost to fulfill the biorefinery plant demands for a given harvest window. Typically, two major issues in these models have been poorly understood: the available time required to complete the harvest operation (often called probability of workdays) and the capacity of the harvest equipment as impacted by yield. Simulations use annual yield estimates, which incorporate weather events, to demonstrate year-to-year effects. Some simulations also incorporate potential yield increases from genetically modified energy crops. There are limited field performance data for most current forage equipment used for harvesting high-yield biomass crops. Analysis shows that the impact of wrap/eject time for round balers resulted in a 50% reduction in achieved throughput capacity (Mg/h). After the maximum throughput is reached, the cost of the round bale operation (3.23 USD/Mg) is double that of the large-square baler (1.63 USD/Mg). The round baler achieved throughput capacity is 50% less (32.7 Mg/h compared to 71.0 Mg/h) than the large-square baler.

09 BIOMASS FUELS↗

NASA NTRS2023

The Statistical Emulators of GGCMI Phase 2: Responses of Year-to-Year Variation of Crop Yield to CO 2 , Temperature, Water and Nitrogen Perturbations

Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO 2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO 2 –temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Climate change↗

DOE OSTI2022

Uncertainties in estimating global potential yields and their impacts for long-term modeling

Estimating realistic potential yields by crop type and region is challenging; such yields depend on both biophysical characteristics (e.g., soil characteristics, climate, etc.), and the crop management practices available in any site or region (e.g., mechanization, irrigation, crop cultivars). A broad body of literature has assessed potential yields for selected crops and regions, using several strategies. In this study we first analyze future potential yields of major crop types globally by two different estimation methods, one of which is based on historical observed yields (“Empirical”), while the other is based on biophysical conditions (“Simulated”). Potential yields by major crop and region are quite different between the two methods; in particular, Simulated potential yields are typically 200% higher than Empirical potential yields in tropical regions for major crops. Applying both of these potential yields in yield gap closure scenarios in a global agro-economic model, GCAM, the two estimates of future potential yields lead to very different outcomes for the agricultural sector globally. In the Simulated potential yield closure scenario, Africa, Asia, and South America see comparatively favorable outcomes for agricultural sustainability over time: low land use change emissions, low crop prices, and high levels of self-sufficiency. In contrast, the Empirical potential yield scenario is characterized by a heavy reliance on production and exports in temperate regions that currently practice industrial agriculture. At the global level, this scenario has comparatively high crop commodity prices, and more land allocated to crop production (and associated land use change emissions) than either the baseline or Simulated potential yield scenarios. This study highlights the importance of the choice of methods of estimating potential yields for agro-economic modeling.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1990

Global climate change and US agriculture

Agricultural productivity is expected to be sensitive to global climate change. Models from atmospheric science, plant science, and agricultural economics are linked to explore this sensitivity. Although the results depend on the severity of climate change and the compensating effects of carbon dioxide on crop yields, the simulation suggests that irrigated acreage will expand and regional patterns of U.S. agriculture will shift. The impact of the U.S. economy strongly depends on which climate model is used.

Adams, Richard M.↗

NASA NTRSDate not supplied

NASA POWER: Providing Analysis-Ready, Cloud-Optimized Data for AI /ML Training and Applications in Earth Science

As global demand for sustainable development grows, the integration of Earth Observation (EO) data into decision making frameworks has become a primary objective for the scientific community. The NASA Prediction of Worldwide Energy Resources (POWER) project serves as a bridge between NASA EO data and the specialized needs of the renewable energy, sustainable infrastructure and agroclimatology communities. In this poster presentation we will present an overview of POWER data products and services along with its use in diverse research to decision-making workflows. By providing over 40 years of high-resolution historical, hourly and daily solar and meteorological data, POWER transforms satellite observations and global model reanalysis into actionable, Analysis-Ready Dataset (ARD). Currently, the project delivers over 250 industry-friendly parameters to the users from different NASA datasets like CERES SYN1Deg, MERRA-2, and IMERG alongside downscaled CMIP6 climate model data, fulfilling over 16 million requests from 50,000 unique users monthly. To ensure data quality and traceability, these parameters are rigorously validated against the ground-based observations from the Baseline Surface Radiation Network (BSRN) and the Global Surface Summary of the Day (GSOD) – these results will be discussed in the presentation. A newly introduced web-based PaRameter Uncertainty ViEwer (PRUVE) tool will be presented that provides an online validation platform to the users that benchmarks satellite-based and assimilation data products against these surface measurements. To reduce technical barriers to data adoption, POWER data is accessible through RESTful APIs, ESRI ArcGIS Image Services, a web-based Data Access Viewer tool, allowing users to visualize, validate and apply the dataset. For efficient data delivery POWER data is cloud-optimized into Zarr datastore accessible through NASA managed Amazon S3 ensures high-performance allowing users to integrate EO directly into operational pipelines. These customized services will be presented. Use cases from application will be presented from the energy sector - such as for design of generation systems, performance monitoring of solar power plants, in infrastructure sector- optimizing building energy efficiency and thermal comfort, in agriculture – such as driving crop simulation and yield forecasting models to enable climate resilient farming. Furthermore, the shift toward machine learning (ML) in EO research that has positioned POWER as a key provider for training datasets which will be discussed. Use-cases will be presented to showcase how NASA data is enabling the development of predictive tools for climate variability and resource management. The poster will present POWER’s future plans including technology development to enhance data traceability and reproducibility and improving I/O performance to support the rapid integration of new EO products, ensuring that POWER remains a robust scalable backend for the evolving landscape of AI-driven Earth Science. Additionally, POWER is developing an AI Agent and an MCP-Server to enable industry AI-Agentic workflows.

Neha Khadka↗

NASA NTRS2017

Integrating Growth Stage Deficit Irrigation into a Process Based Crop Model

Current rates of agricultural water use are unsustainable in many regions, creating an urgent need to identify improved irrigation strategies for water limited areas. Crop models can be used to quantify plant water requirements, predict the impact of water shortages on yield, and calculate water productivity (WP) to link water availability and crop yields for economic analyses. Many simulations of crop growth and development, especially in regional and global assessments, rely on automatic irrigation algorithms to estimate irrigation dates and amounts. However, these algorithms are not well suited for water limited regions because they have simplistic irrigation rules, such as a single soil-moisture based threshold, and assume unlimited water. To address this constraint, a new modeling framework to simulate agricultural production in water limited areas was developed. The framework consists of a new automatic irrigation algorithm for the simulation of growth stage based deficit irrigation under limited seasonal water availability; and optimization of growth stage specific parameters. The new automatic irrigation algorithm was used to simulate maize and soybean in Gainesville, Florida, and first used to evaluate the sensitivity of maize and soybean simulations to irrigation at different growth stages and then to test the hypothesis that water productivity calculated using simplistic irrigation rules underestimates WP. In the first experiment, the effect of irrigating at specific growth stages on yield and irrigation water use efficiency (IWUE) in maize and soybean was evaluated. In the reproductive stages, IWUE tended to be higher than in the vegetative stages (e.g. IWUE was 18% higher than the well watered treatment when irrigating only during R3 in soybean), and when rainfall events were less frequent. In the second experiment, water productivity (WP) was significantly greater with optimized irrigation schedules compared to non-optimized irrigation schedules in water restricted scenarios. For example, the mean WP across 38 years of maize production was 1.1 kg/cu m for non-optimized irrigation schedules with 50 mm of seasonal available water and 2.1 kg/cu m optimized ion schedules, a 91% improvement in WP with optimized irrigation schedules. The framework described in this work could be used to estimate WP for regional to global assessments, as well as derive location specific irrigation guidance.

crop model↗

NASA NTRS1982

Second Generation Crop Yield Models Review

Second generation yield models, including crop growth simulation models and plant process models, may be suitable for large area crop yield forecasting in the yield model development project. Subjective and objective criteria for model selection are defined and models which might be selected are reviewed. Models may be selected to provide submodels as input to other models; for further development and testing; or for immediate testing as forecasting tools. A plant process model may range in complexity from several dozen submodels simulating (1) energy, carbohydrates, and minerals; (2) change in biomass of various organs; and (3) initiation and development of plant organs, to a few submodels simulating key physiological processes. The most complex models cannot be used directly in large area forecasting but may provide submodels which can be simplified for inclusion into simpler plant process models. Both published and unpublished models which may be used for development or testing are reviewed. Several other models, currently under development, may become available at a later date.

Hodges, T.↗

DOE OSTI2023

Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020

Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates is largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields (> 250 m). Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000-2020 across a 12 state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015-2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forests model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008-2014. We also evaluated the full 2000-2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R 2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R 2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000-2020, each standard deviation increase in rainfall delayed planting by ~ 2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

Uncertainty in Simulating Wheat Yields Under Climate Change

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

exhaust gases↗