Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt

This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions

Forecasting yields and in-season crop-water nitrogen needs using simulation models

Forecasting crop yields and water-nitrogen dynamics during the growing cycle of the crops can greatly advance in-season decision making processes. To date, forecasting approaches include the use of statistical or mechanistic simulation models, aerial images, or combinations of these to make the predictions. Different approaches and models have different capabilities, strengths, and limitations. System-level mechanistic simulation models (crop and soil models together) usually offer more prediction and explanatory power at the cost of extensive input data. In contrast, statistical approaches or aerial images can be more robust than mechanistic models but their applicability and prediction/explanatory power is limited. The combination of these technologies is viewed as a very promising tool to assist Midwestern agriculture, but in general, all of these technologies are in their initial stages of implementation and more time is needed to prove their potential. Here we present results from a pilot project that aimed to forecast weather, soil water-nitrogen status, crop water-nitrogen demand, and end-of-season crop yields in Iowa using two process-based mechanistic simulation models

Understanding the 2016 yields and interactions between soils, crops, climate and management

Several technologies to forecast crop yields and soil nutrient dynamics have emerged over the past years. These include process-based models, statistical models, machine learning, aerial images, or combinations. These technologies are viewed as promising to assist Midwestern agriculture to achieve production and environmental goals, but in general, most of these technologies are in their initial stages of implementation. In June 2016 we launched a web-tool (http://crops.extension.iastate.edu/facts/) that provided real-time information and yield predictions for 20 combinations of crops and management practices. Our project, which is called FACTS (Forecast and Assessment of Cropping sysTemS), takes a systems approach to forecast and evaluate cropping systems performance. In this paper we report FACTS yield predictions accuracy against ground-truth measurements and analyzing factors responsible for achieving 200-240 bu/acre corn yield and 55-75 bu/acre soybean yields in the FACTS plots in 2016

Water availability, root depths and 2017 crop yields

During 2016 and 2017, June-July precipitation was below normal in many parts of Iowa creating midseason concerns about potential yield loss due to water stress. However, these concerns were not realized. In contrast, 2016 and 2017 crop yields over-performed yields obtained in many years with average of above average June-July precipitation. In Iowa, deep root systems, high soil water storage capacity, and shallow water tables are common explanations for high yields in years with below normal precipitation. How deep can roots grow? How much does groundwater contribute to the yields? To answer these questions and more, the Forecast and Assessment of Cropping sysTemS (FACTS) project was established in 201

In search of the authentic nation: landscape and national identity in Canada and Switzerland

While the study of nationalism and national identity has flourished in the last decade, little attention has been devoted to the conditions under which natural environments acquire significance in definitions of nationhood. This article examines the identity-forming role of landscape depictions in two polyethnic nation-states: Canada and Switzerland. Two types of geographical national identity are identified. The first – what we call the ‘nationalisation of nature’– portrays zarticular landscapes as expressions of national authenticity. The second pattern – what we refer to as the ‘naturalisation of the nation’– rests upon a notion of geographical determinism that depicts specific landscapes as forces capable of determining national identity. The authors offer two reasons why the second pattern came to prevail in the cases under consideration: (1) the affinity between wild landscape and the Romantic ideal of pure, rugged nature, and (2) a divergence between the nationalist ideal of ethnic homogeneity and the polyethnic composition of the two societies under consideration

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment

Impacts of climate change on the optimum planting date of different maize cultivars in the central US Corn Belt

Planting date and cultivar selection are major factors in determining the yield potential of any crop and in any region. However, there is a knowledge gap in how climate scenarios affect these choices. To explore this gap, we performed a regional scale analysis (11 planting dates x 8 cultivars x 281 fields x 36 weather years x 6 climate scenarios) using the APSIM model and pSIMS software for Iowa, the leading US maize (Zea mays L.) producing state. Our objectives were to determine how the optimum planting date (optPD) changes with weather scenarios and cultivars and the potential economic implications of planting outside the optimum windows. Results indicated that the mean optPD corresponds to the US Department of Agriculture, National Agriculture Statistics Service (USDA-NASS) 18.4% planting progress (April 28th) in Iowa. The optPD was found to be advancing by –0.13 d yr-1 from 1980 to 2015. A 1oC increase in mean temperature increased the length of the growing season by 10 days while the optPD changed by –2 to + 6 days, depending on cultivar. Under a more realistic scenario of increasing the minimum temperature by 0.5oC, decreasing the maximum temperature by 0.5oC, increasing spring rainfall by 10% and decreasing summer rainfall by 10%, the optPD only changed by –2 days compared to current trends, however, yield increased by 6.6%. Analysis of historical USDA-NASS planting durations indicated that on average, the planting duration (1% to 99% statewide reported planting progress) is 44 days, while it can be as low as 21 days in years with favorable weather. A simple economic analysis illustrated a potential revenue loss up to $340 million per year by planting maize outside the optimum window. We conclude that future investments in planting technologies to accelerate planting, especially in challenging weather years, as well as improved optPD x cultivar recommendations to farmers, will provide economic benefits and buffer climate variability.This is a manuscript of an article published as Baum, Mitch E., Mark A. Licht, Isaiah Huber, and Sotirios V. Archontoulis. "Impacts of climate change on the optimum planting date of different maize cultivars in the central US Corn Belt." European Journal of Agronomy 119 (2020): 126101. doi:10.1016/j.eja.2020.126101. Posted with permission. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License

A time-dependent parameter estimation framework for crop modeling

The performance of crop models in simulating various aspects of the cropping system is sensitive to parameter calibration. Parameter estimation is challenging, especially for time-dependent parameters such as cultivar parameters with 2–3 years of lifespan. Manual calibration of the parameters is time-consuming, requires expertise, and is prone to error. This research develops a new automated framework to estimate time-dependent parameters for crop models using a parallel Bayesian optimization algorithm. This approach integrates the power of optimization and machine learning with prior agronomic knowledge. To test the proposed time-dependent parameter estimation method, we simulated historical yield increase (from 1985 to 2018) in 25 environments in the US Corn Belt with APSIM. Then we compared yield simulation results and nine parameter estimates from our proposed parallel Bayesian framework, with Bayesian optimization and manual calibration. Results indicated that parameters calibrated using the proposed framework achieved an 11.6% reduction in the prediction error over Bayesian optimization and a 52.1% reduction over manual calibration. We also trained nine machine learning models for yield prediction and found that none of them was able to outperform the proposed method in terms of root mean square error and R2. The most significant contribution of the new automated framework for time-dependent parameter estimation is its capability to find close-to-optimal parameters for the crop model. The proposed approach also produced explainable insight into cultivar traits’ trends over 34 years (1985–2018).This article is published as Akhavizadegan, Faezeh, Javad Ansarifar, Lizhi Wang, Isaiah Huber, and Sotirios V. Archontoulis. "A time-dependent parameter estimation framework for crop modeling." Scientific Reports 11, no. 1 (2021): 11437. DOI: 10.1038/s41598-021-90835-x Copyright 2021 The Author(s). Attribution 4.0 International (CC BY 4.0). Posted with permission

County-scale crop yield prediction by integrating crop simulation with machine learning models

Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield prediction complex. Building upon a previous work in which we coupled crop modeling with machine learning (ML) models to predict maize yields for three US Corn Belt states, here, we expand the concept to the entire US Corn Belt (12 states). More specifically, we built five new ML models and their ensemble models, considering the scenarios with and without crop modeling variables. Additional input values in our models are soil, weather, management, and historical yield data. A unique aspect of our work is the spatial analysis to investigate causes for low or high model prediction errors. Our results indicated that the prediction accuracy increases by coupling crop modeling with machine learning. The ensemble model overperformed the individual ML models, having a relative root mean square error (RRMSE) of about 9% for the test years (2018, 2019, and 2020), which is comparable to previous studies. In addition, analysis of the sources of error revealed that counties and crop reporting districts with low cropland ratios have high RRMSE. Furthermore, we found that soil input data and extreme weather events were responsible for high errors in some regions. The proposed models can be deployed for large-scale prediction at the county level and, contingent upon data availability, can be utilized for field level prediction.This article is published as Sajid, Saiara Samira, Mohsen Shahhosseini, Isaiah Huber, Guiping Hu, and Sotirios Archontoulis. "County-scale crop yield prediction by integrating crop simulation with machine learning models." Frontiers in Plant Science 13 (2022): 4841. DOI: 10.3389/fpls.2022.1000224. Copyright 2022 Sajid, Shahhosseini, Huber, Hu and Archontoulis. Attribution 4.0 International (CC BY 4.0). Posted with permission