PREDICTION OF CROP YIELDS ACROSS FOUR CLIMATE ZONES IN GERMANY: AN ARTIFICIAL NEURAL NETWORK APPROACH

This paper shows the ability of artificial neural network technology to be used for the approximation and prediction of crop yields at rural district and federal state scales in different climate zones based on reported daily weather data. The method may later be used to construct regional time series of agricultural output under climate change, based on the highly resolved output of the global circulation models and regional models. Three 30-year combined historical data sets of rural district yields (oats, spring barley and silage maize), daily temperatures (mean, maximum, dewpoint) and precipitation were constructed. They were used with artificial neural network technology to investigate, simulate and predict historical time series of crop yields in four climate zones of Germany. Final neural networks, trained with data sets of three climate zones and tested against an independent northern zone, have high predictive power (0.83global change, agriculture, artificial neural networks, yield prediction

Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting

Crop yield forecasting is critical in modern agriculture to ensure food security, economic stability, and effective resource management. The main goal of this study was to combine historical multisource satellite and environmental datasets with a deep learning (DL) model for soybean yield forecasting in the United States’ Corn Belt. The following Moderate Resolution Imaging Spectroradiometer (MODIS) products were aggregated at the county level. The crop data layer (CDL) in Google Earth Engine (GEE) was used to mask the data so that only soybean pixels were selected. Several machine learning (ML) models were trained by using 5 years of data from 2012 to 2016: random forest (RF), least absolute shrinkable and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and decision tree regression (DTR) as well as DL-based one-dimensional convolutional neural network (1D-CNN). The best model was determined by comparing their performances at forecasting the soybean yield in 2017–2021 at the county scale. The RF model outperformed all other ML models with the lowest RMSE of 0.342 t/ha, followed by XGBoost (0.373 t/ha), DTR (0.437 t/ha), and LASSO (0.452 t/ha) regression. However, the 1D-CNN model showed the highest forecasting accuracy for the 2018 growing season with RMSE of 0.280 t/ha. The developed 1D-CNN model has great potential for crop yield forecasting because it effectively captures temporal dependencies and extracts meaningful input features from sequential data

Detection and attribution of cereal yield losses using Sentinel-2 and weather data: A case study in South Australia

Weather extremes affect crop production. Remote sensing can help to detect crop damage and estimate lost yield due to weather extremes over large spatial extents. We propose a novel scalable method to predict in-season yield losses at the sub-field level and attribute these to weather extremes. To assess our method’s potential, we conducted a proof-of-concept case study on winter cereal paddocks in South Australia using data from 2017 to 2022. To detect crop growth anomalies throughout the growing season, we aligned a two-band Enhanced Vegetation Index (EVI2) time series from Sentinel-2 with thermal time. The deviation between the expected and observed EVI2 time series was defined as the Crop Damage Index (CDI). We assessed the performance of the CDI within specific phenological windows to predict yield loss. Finally, by comparing instances of substantial increase in CDI with different extreme weather indicators, we explored which (combinations of) extreme weather events were likely responsible for the experienced yield reduction. We found that the use of thermal time diminished the temporal deviation of EVI2 time series between years, resulting in the effective construction of typical stress-free crop growth curves. Thermal-time-based EVI2 time series resulted in better prediction of yield reduction than those based on calendar dates. Yield reduction could be predicted before grain-filling (approximately two months before harvest) with an R2 of 0.83 for wheat and 0.91 for barley. Finally, the combined analysis of CDI curves and extreme weather indices allowed for timely detection of weather-related causes of crop damage, which also captured the spatial variations of crop damage attribution at sub-field level. The proposed framework provides a basis for early warning of crop damage and attributing the damage to weather extremes in near real-time, which should help to adopt appropriate crop protection strategies

Climatological, Hydrological, and Economic Analysis of Agriculture in Montana and the Western U.S.A.

Many studies have addressed the impact of climate on agriculture; however, fewer studies addressed how farmers adapt to climate change, to what extent implementation of adaptation strategies mitigates economic losses or alters the hydrologic system. Analyses of how historical climate affected not only farmer decision making, but also the economic and hydrological consequences of farmers’ adaptations to climate variations, and projections of the spatiotemporal climatic regimes at finer regional scales are critical for aiding in actionable climate change adaptations. This dissertation helps fill knowledge gaps on the impacts of climate change in rural regions of the agricultural western U.S.A. and provides a baseline to understand what crops farmers in the region will prioritize under future climates, and what will be the economic and hydrologic costs of adaptation. The first project modeled producer behavior under end-of-century climate projections. We applied a stochastic, integrated hydro-economic model that simulates land and water allocations to analyze Montana farmer adaptations to a range of projected climate conditions and the response of the hydrologic system to those adaptations. Results show a state-wide increase in agricultural water use leading to decreased summer streamflows. Land use for irrigated crops increased while rainfed crops decreased, implying state-level decrease in planted area. Both irrigated and rainfed crop production and farmer revenue decreased. The second project used historical data to quantify the climate water deficit (CWD) threshold where farmers’ perception swings towards repurposing crops instead of harvesting for grain. We analyzed the relationship between crop repurposing (the ratio of acres harvested for grain to the total planted acres) to seasonal CWD, and to isolate the climate signal from economic factors, our analysis accounted for the influence of crop prices on grain harvest. Results indicate that farmers are less likely to harvest barley and spring wheat for grain when the spring CWD is above average. For the majority of major crop growing regions, grain prices increased with lower levels of grain harvest. The third project used the most current climate change forecasts to predict future climate regimes of important rainfed winter wheat growing regions and compare current yields of climate analog regions. Using a suite of climate models, we evaluate which model(s) best simulated seasonal historical distributions of five climatic variables using the energy distance statistical metric, then use the best performing models to predict and map mid-century climate analog locations across the western U.S.A. Results show significant western and/or southern shifts in analog locations, regardless of season. These shifts to warmer, dryer regions do not conclusively imply decreased yields, however land use devoted to rainfed winter wheat in analog regions was dramatically lower

A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development

Modeling the Global Water Resource System in an Integrated Assessment Modeling Framework: IGSM-WRS

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/)The availability of water resources affects energy, agricultural and environmental systems, which are linked together as well as to climate via the water cycle. As such, watersheds and river basins are directly impacted by local and regional climate variations and change. In turn, these managed systems provide direct inputs to the global economy that serve and promote public health, agricultural and energy production, ecosystem surfaces and infrastructure. We have enhanced the Integrated Global System Model (IGSM) framework capabilities to model effects on the managed water-resource systems of the influence of potential climate change and associated shifts in hydrologic variation and extremes (i.e. non-stationarity in the hydro-climate system), and how we may be able to adapt to these impacts. A key component of this enhancement is the linkage of the Water Resources System (WRS) into the IGSM framework. WRS is a global river basin scale model of water resources management, agricultural (rain-fed and irrigated crops and livestock) and aquatic environmental systems. In particular, WRS will provide the capability within the IGSM framework to explore allocation of water among irrigation, hydropower, urban/industrial, and in-stream uses and investigate how society might adapt water resources due to shifts in hydro-climate variations and extremes. This paper presents the overall design of WRS, its linkages to the land system and economic models of the IGSM, and results of test bed runs of WRS components to address issues of temporal and spatial scales in these linkages.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Climate change, agriculture and Fairtrade: identifying the challenges and opportunities

This paper presents the findings of a study commissioned by the UK's Fairtrade Foundation on the implications of climate change for agricultural producers in Fairtrade value chains

Atlas of Global Change Risk of Population and Economic Systems

This book is open access and illustrates the spatial distribution of the global change risk of population and economic systems with the maps of environment, global climate change, global population and economic systems, and global change risk. The risks of global change are mapped at 0.25 degree grid unit. The risk results and their contribution rates of the world at national level are unprecedentedly derived and ranked. The book can be a good reference for researchers and students in the field of global climate change and natural disaster risk management, as well as risk managers and enterpriser to understand the global change risk of population and economic systems