A CNN-RNN Framework for Crop Yield Prediction

Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully-connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN have three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.Comment: 26 Pages, 14 Figure

Development of a nitrogen recommendation tool for corn considering static and dynamic variables

Many soil and weather variables can affect the economical optimum nitrogen (N) rate (EONR) for maize. We classified 54 potential factors as dynamic (change rapidly over time, e.g. soil water) and static (change slowly over time, e.g. soil organic matter) and explored their relative importance on EONR and yield prediction by analyzing a dataset with 51 N trials from Central-West region of Argentina. Across trials, the average EONR was 113 ± 83 kg N ha−1 and the average optimum yield was 12.3 ± 2.2 Mg ha−1, which is roughly 50% higher than the current N rates used and yields obtained by maize producers in that region. Dynamic factors alone explained 50% of the variability in the EONR whereas static factors explained only 20%. Best EONR predictions resulted by combining one static variable (soil depth) together with four dynamic variables (number of days with precipitation\u3e20 mm, residue amount, soil nitrate at planting, and heat stress around silking). The resulting EONR model had a mean absolute error of 39 kg N ha−1 and an adjusted R2 of 0.61. Interestingly, the yield of the previous crop was not an important factor explaining EONR variability. Regression models for yield at optimum and at zero N fertilization rate as well as regression models to be used as forecasting tools at maize planting time were developed and discussed. The proposed regression models are driven by few easy to measure variables filling the gap between simple (minimum to no inputs) and complex EONR prediction tools such as simulation models. In view of increasing data availability, our proposed models can be further improved and deployed across environments. Includes supplemental figures and table. Excel model attached below as additional file

Simple Models for Describing Ruminant Herbivory

The use of quantitative independent variables in experiments allows the use of regression to explore the functional relationship between treatments applied and measured responses. It provides the opportunity to not only understand the magnitude and importance of the response but also ascertain its nature. The simplest approach is to fit a polynomial. While it is often possible to obtain a very good fit using this approach, it offers in the way of providing insight into the response. At best, you can determine if the response is nonlinear and if so, if it is complex or not. The model parameters are empirical and generally cannot be interpreted as having any biological, chemical, or physical meaning—at least not directly. There are situations, however, when such a meaning can be inferred from a model fit using simple regression. In general, this is true when the relationship is truly linear or when a nonlinear model can be considered to be “intrinsically” linear; that is, it can be linearized by transforming the data in a way that can be fit using simple linear regression. A series of forage quality examples are used to illustrate these concepts in this article

Simulated dataset of corn response to nitrogen over thousands of fields and multiple years in Illinois

Nitrogen (N) fertilizer recommendations for corn (Zea mays L.) in the US Midwest have been a puzzle for several decades, without agreement among stakeholders for which methodology is the best to balance environmental and economic outcomes. Part of the reason is the lack of long-term data of crop responses to N over multiple fields since trial data is often limited in the number of soils and years it can explore. To overcome this limitation, we designed an analytical platform based on crop simulations run over millions of farming scenarios over extensive geographies. The database was calibrated and validated using data from more than four hundred trials in the region. This dataset can have an important role for research and education in N management, machine leaching, and environmental policy analysis. The calibration and validation procedure provides a framework for future gridded crop model studies. We describe dataset characteristics and provide thorough descriptions of the model setup

Long term biochar effects on corn yield, soil quality and profitability in the US Midwest

Corn production in the US Midwest has the potential to generate a large amount of crop residue for bioenergy production. However, unconstrained harvesting of crop residues is associated with a long-term decline in soil quality. Biochar applications can mitigate many of the negative effects of residue removal but data and economic analyses to support decision making are lacking. To explore sustainable and profitable practices for residue harvesting in central Iowa we used 11 years of soil, crop yield, and management data to calibrate the Agricultural Production Systems sIMulator (APSIM) biochar model. We then used the model to evaluate how different biochar types and application rates impact productivity and environmental performance of conventional corn and corn-soybean cropping systems in Iowa under different N fertilizer application rates and residue harvesting scenarios. A cost-benefit analysis was also employed to identify the economically optimal biochar application rate from both producer and societal perspectives. Modeling results showed for both continuous corn and corn-soybean rotations that as biochar application rate increased (from 0 to 90 Mg ha-1) nitrate leaching decreased (from 2.5 to 20 %) and soil carbon levels increased (from 8 to 115 %), but there was only a small impact on corn yields (from –2.6 to 0.6 %). The cost-benefit analysis revealed that public benefits, evaluated from decreased nitrate leaching and increased soil carbon levels, significantly outweighed the private revenue accrued from crop yield gains, and that a biochar application rate of 22 Mg ha-1 was more cost-effective (per ton) compared to higher biochar rates. Overall, this study found that applying biochar once at a rate of 22 Mg ha-1 allows for the sustainable annual removal of 50% of corn residue for 32 years, is profitable for farmers even with minimal impact on grain yield, and beneficial to society through reduced nitrate leaching and increased soil organic carbon levels