Satellite forecasting of crop harvest can trigger a cross-hemispheric production response and improve global food security

Global food security is increasingly threatened by climate change and regional human conflicts. Abnormal fluctuations in crop production in major exporting countries can cause volatility in food prices and household consumption in importing countries. Here we show that timely forecasting of crop harvest from satellite data over major exporting regions can trigger production response in the opposite hemisphere to offset the short-term fluctuations and stabilize global food supply. Satellite forecasting can reduce the fluctuation extents of country-level prices by 1.1 to 12.5 percentage points for anticipated wheat shortage or surplus in Russia and Ukraine, and even reverse the price shock in importing countries for anticipated soybean shortage in Brazil. Our research demonstrates that by leveraging the seasonal lags in crop calendars between the Northern and Southern Hemispheres, operational crop monitoring from satellite data can provide a mechanism to improve global food security

Climatic stresses and rural emigration in Guatemala

International migration is a recurrent and growing phenomenon and a large share of emigrants originate from rural areas. This study examines the association between climatic stresses and rural emigration in Guatemala. We exploit variations on climatic stress situations and emigration flows at the subnational level and over time to examine whether the observed migration dynamics can be explained by the occurrence of specific adverse weather events. We find that drought periods affect emigration positively the following year, especially among men, while periods of high temperatures and low soil moisture affect male and female emigration negatively. The results are generally not much sensitive to alternative model specifications and estimations. The apparent mixed findings point to both direct effects where climatic stresses may encourage people to migrate in search of better opportunities, as well as indirect effects in the sense that climatic stresses affect agricultural productivity and household liquidity, which may prevent people from migrating despite their willingness to emigrate

Corn and Soybean Production Costs and Export Competitiveness in Argentina, Brazil, and the United States

This report explores export competitiveness of soybeans and corn in Argentina, Brazil, and the United States by comparing farm-level production costs, the cost of internal transportation and handling, and the cost of shipping to a common export destination. In addition, prices received by farmers and average yields for each crop in each country are analyzed to calculate producer returns. Errata: This report was revised in July 2016 by correcting table 5, which now corresponds with the text. The table includes two Brazilian regions for each commodity, additional rows that show two components of the farm price, and the correct transportation costs for Brazil

Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina

Accurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance companies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth progress, and this progress can be monitored in-season using satellite data. Here, we use satellite derived metrics, from both optical and radar satellites, and machine learning models to model field-scale crop yields for over 3,000 Soybean and Wheat in Argentina. We compare several machine learning models and our results show the promise of combining mixed effect models with non-parametric models in improving yield modeling capabilities. We also demonstrate the utility of specific satellite derived metrics and extracted features in improving model performance and show that our approach can explain greater than 70% of the variation in yields while remaining generalizable across crops and agro-ecological zones.Sociedad Argentina de Informática e Investigación Operativ

Evaluating the Impact of the 2020 Iowa Derecho on Corn and Soybean Fields Using Synthetic Aperture Radar

On 10 August 2020, a series of intense and fast-moving windstorms known as a derecho caused widespread damage across Iowa’s (the top US corn-producing state) agricultural regions. This severe weather event bent and flattened crops over approximately one-third of the state. Immediate evaluation of the disaster’s impact on agricultural lands, including maps of crop damage, was critical to enabling a rapid response by government agencies, insurance companies, and the agricultural supply chain. Given the very large area impacted by the disaster, satellite imagery stands out as the most efficient means of estimating the disaster impact. In this study, we used time-series of Sentinel-1 data to detect the impacted fields. We developed an in-season crop type map using Harmonized Landsat and Sentinel-2 data to assess the impact on important commodity crops. We intersected a SAR-based damage map with an in-season crop type map to create damaged area maps for corn and soybean fields. In total, we identified 2.59 million acres as damaged by the derecho, consisting of 1.99 million acres of corn and 0.6 million acres of soybean fields. Also, we categorized the impacted fields to three classes of mild impacts, medium impacts and high impacts. In total, 1.087 million acres of corn and 0.206 million acres of soybean were categorized as high impacted fields.https://doi.org/10.3390/rs1223387