Model for soybean production forecast based on prevailing physical conditions

Abstract

-The objective of this work was to evaluate the reliability of the physiological meaning of the enhanced vegetation index (EVI) data for the development of a remote sensing-based procedure to estimate soybean production prior to crop harvest. Time-series data from the moderate resolution imaging spectroradiometer (Modis) were applied to investigate the relationship between local yield fluctuations of soybean and the prevailing physically-driven conditions in the state of Mato Grosso, located in the south of the Brazilian Amazon. The developed methodology was based on the coupled model (CM). The CM provides production estimates for early January, using images from the maximum crop development period. Production estimates were validated at three different spatial scales: state, municipality, and local. At the state and municipality levels, the results obtained from the CM were compared with official agricultural statistics from Instituto Brasileiro de Geografia e Estatística and Companhia Nacional de Abastecimento, from 2001 to 2011. The coefficients of determination ranged from 0.91 to 0.98, with overall result of R 2 =0.96 (p≤0.01), indicating that the model adheres to official statistics. At the local level, spatially distributed data were compared with production data from 422 crop fields. The coefficient of determination (R 2 =0.87) confirmed the reliability of the EVI for its applicability on remote sensing-based models for soybean production forecast.Index terms: agriculture, EVI, Modis, remote sensing, satellite. Modelo para previsão da produção de soja baseado em condições físicas predominantesResumo -O objetivo deste trabalho foi avaliar a confiabilidade do significado fisiológico de dados do índice de vegetação "enhanced vegetation index" (EVI) no desenvolvimento de um procedimento baseado em sensoriamento remoto para estimar a produção de soja antes da colheita. Foram aplicados dados de séries temporais do "moderate resolution imaging spectroradiometer" (Modis) para investigar a relação entre as flutuações locais na produtividade da soja e as condições físicas predominantes no Estado de Mato Grosso, localizado no sul da Amazônia brasileira. A metodologia desenvolvida foi baseada no modelo acoplado (CM). O CM fornece estimativas de produção para o início de janeiro, ao utilizar imagens do período de máximo desenvolvimento da cultura. ,87) confirmou a confiabilidade do EVI para ser aplicado em modelos baseados em sensoriamento remoto, para previsão da produção de soja.Termos para indexação: agricultura, EVI, Modis, sensoriamento remoto, satélite.

Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

“…This study also emphasizes the capacity of remote sensing-based rainfall estimation products to highlight a few subregions such as the Serra do Cachimbo (southern Pará state) or the Chapada dos Parecis (western Mato Grosso state) that do not stand out in studies based on in-situ data. This ability to capture the spatio-temporal variability of rainfall regimes in the Amazon is a major advantage of remote sensing products for fine-scale crop monitoring [67] or hydrological modeling [55,68].…”

Analysis of soybean cropland expansion in the southern Brazilian Amazon and its relation to economic drivers

“…For this reason, the use of a fixed calendar date to study remote sensing-based yield-prediction models is not optimal (Bolton and Friedl 2013;Gusso et al 2017). We applied the MCDA approach by using MODIS/Terra EVI (i.e., the Enhanced Vegetation Index) data at a 250-m spatial resolution and a 16-day temporal resolution (MOD13Q1 product).…”

“…Although it is expected that MCDA can easily detect soybean crops in consolidated or recurring areas, as shown in studies by Gusso et al (2012), Gusso et al (2014), and Gusso et al (2017), agricultural practices and dynamics in MT are evolving rapidly. Regarding the prevailing physically driven conditions in the last decade, intensive practices such as double cropping have been widely adopted in Mato Grosso, which is an additional challenge for accurate crop area mapping (Arvor et al 2012a).…”

Canopy temperatures distribution over soybean crop fields using satellite data in the Amazon biome frontier

“…The sowing period for soybeans lasts from mid-September to early December (Arvor et al, 2012;Gusso et al, 2017a). Farmers plant soybeans after the onset of the rainy season, usually in October and soybeans remain the main crop, while maize or cotton is planted after the soybean harvest (Arvor, Jonathan, Meirelles, Dubreuil, & Durieux, 2011;Arvor et al, 2014).…”

“…Nowadays, soybean cultivation in Mato Grosso covers almost 8.0 million ha and the production level was more than 23.5 million metric tons in 2013 reaching a yield average of 3220 kg ha -1 , with 2930 kg ha -1 between 2003 and 2010 (IBGE -Instituto Brasileiro de Geografia e Estatística, 2013). From these expectations emerges the need for a better understanding of the impacts caused from recent environmental conditions on crop yield (Gusso, Arvor, & Ducati, 2017a). The objective of this study was to investigated specific characterization of land surface temperature distribution over soybean crop fields due to field homogeneity of massive land use conversion into soybean areas and its impacts on yield in the Mato Grosso (MT) state, Brazil.…”