Crop Modeling With Less Data: The Fao Model for Soybean Yield Estimation

Richetti, Jonathan; Johann, Jerry Adriani; Uribe-Opazo, Miguel Ángel

doi:10.1590/1809-4430-eng.agric.v41n2p196-203/2021

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Currently, they play a fundamental role in understanding variables related to the most diverse areas of knowledge, such as public health (Silva et al, 2020), climatology (Barboza et al, 2020), and aquaculture (Francisco et al, 2020). In the Western Mesoregion of Paraná, located in the Southern region of Brazil, there are works related to precision farming, aimed at understanding the spatial distribution of soybean productivity (Dalposso et al, 2021;Richetti et al, 2021;Silva et al, 2021). Soybean (Glycine max (L.) Merril) is one of the world's main agricultural crops (CONAB, 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of soybean yield in the western mesoregion of Paraná using agrometeorological variables

Gabriel

Uribe-Opazo

Dalposso

et al. 2022

RSD

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This paper aimed to analyze the spatial autocorrelation of soybean yield and its bivariate spatial correlation with theagrometeorological variables rainfall, mean temperature, and mean global solar radiation in 2014/2015, 2015/2016, and 2016/2017 crop years in the West of the State of Paraná – Brazil. To achieve this objective, techniques of spatial statistics of areas were used, which, through autocorrelation and spatial correlation indices, sought to identify patterns of association between soybean yield and agrometeorological variables. This research is justified because in addition to the soybean crop being the main source of food protein and vegetable oil in the world and the agrometeorological variables being the factors that most influence it, the western mesoregion of Paraná stands out with the highest production values in the state. Thus, it is important to monitor its development through spatial analysis to obtain information that will support decision making. The global and local Moran’s indices showed that soybean yield is self-correlated in the municipalities of Western Paraná, identifying clusters to the west and east of the mesoregion. The significance of the bivariate spatial correlation indices confirmed the influence of the variables rainfall, mean temperature, and average global solar radiation on soybean yield.

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Section: Introductionmentioning

confidence: 99%

Spatial analysis of soybean yield in the western mesoregion of Paraná using agrometeorological variables

Gabriel

Uribe-Opazo

Dalposso

et al. 2022

RSD

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Calibration and evaluation of CSM-CROPGRO-soybean for soybean crop in the southwestern cerrado of Piauí

Irene Filho,

Andrade Júnior,

Cuadra

et al. 2024

Rev. Caatinga

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The study aimed to calibrate and evaluate the DSSAT CSM-CROPGRO-Soybean model to simulate soybean grain yields in the Cerrado of the Southwestern region of Piaui. To parameterize the model, data from the 2019-2020 crop season was used from an experiment installed in the Serra do Quilombo, in Bom Jesus-PI (9º16'20.3'' S, 44º44'56.9'' O, and altitude 620 m). The BRS 8980 IPRO (BRS 8980), BMX 84I86 (Domínio), BMX 81I81RSF IPRO (Extrema), and BMX 8579 IPRO (Bonus) cultivars were evaluated on three sowing dates (11/29/2019, 01/14/2020, and 01/30/2020). The evaluation was conducted using soybean yield data collected in value for cultivation and use (VCU) experiments conducted by Embrapa Meio-Norte at Celeiro farm, Serra do Quilombo, Bom Jesus, PI, during four harvests and involving 61 genotypes. The best statistical indexes showing the efficiency of the calibration process were observed for the BRS 8980 (first sowing season) and Bônus (third sowing season) cultivars, with R2 and D indexes above 0.90. The total biomass production showed high agreement with the measured values, capturing the decrease in production due to the sowing date. The model captured the variability depending on the sowing dates and the yield for simulations of four other agricultural seasons, independent of the season in which the model was calibrated. It was concluded that the model satisfactorily simulated plant growth and soybean grain yield for the conditions of the Cerrado of the Southwestern region of Piaui.

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Crop Modeling With Less Data: The Fao Model for Soybean Yield Estimation

Cited by 2 publications

References 17 publications

Spatial analysis of soybean yield in the western mesoregion of Paraná using agrometeorological variables

Spatial analysis of soybean yield in the western mesoregion of Paraná using agrometeorological variables

Calibration and evaluation of CSM-CROPGRO-soybean for soybean crop in the southwestern cerrado of Piauí

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