Agrometeorological analysis of the soybean potentiality in an Amazonian environment

Lima, Marcus José Alves de; Oliveira, Evandro Chaves de; Sampaio, Leila Sobral; Fraisse, Clyde W.; Souza, Paulo de Oliveira Ponte de Jorge

doi:10.1590/1983-40632019v4954595

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…CSM-CROPGRO-Soybean demonstrated good performance for both MG 7.7 and 8.8 in response to plant densities and sowing dates in this short photoperiod low latitude environment. The performance of the model as described by statistical indices are similar to prior studies in Brazil for the north (Lima et al, 2019), midwestern (Teixeira et al, 2019) and southern (Battisti et al, 2017) regions. Furthermore, a characteristic not accounted for by Grimm et al (1993) in the default parameters is the presence of long juvenile phase introduced into soybean germplasm adapted to low latitude (Destro et al, 2001;Carpentieri-Pípolo et al, 2002;Sinclair et al, 2005;Alliprandini et al, 2009;Liu et al, 2017).…”

Section: Discussionsupporting

confidence: 78%

“…The DSSAT CSM-CROPGRO-Soybean model v 4.7.0 (Hoogenboom et al, 2017) is widely used in Brazil for management evaluation, for example, for tropical conditions (Banterng et al, 2009), climate response (Silva et al, 2017;Battisti and Sentelhas, 2019;Lima et al, 2019) and irrigation (Battisti et al, 2020b). The response to plant density is influenced by the LAI which affects crop evapotranspiration and photosynthesis rates during the life cycle.…”

Section: Description Of Plant Density Responsementioning

confidence: 99%

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Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

et al. 2020

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Crop models can be used to explain yield variations associated with management practices, environment and genotype. This study aimed to assess the effect of plant densities using CSM-CROPGRO-Soybean for low latitudes. The crop model was calibrated and evaluated using data from field experiments, including plant densities (10, 20, 30 and 40 plants per m2), maturity groups (MG 7.7 and 8.8) and sowing dates (calibration: 06 Jan., 19 Jan., 16 Feb. 2018; and evaluation: 19 Jan. 2019). The model simulated phenology with a bias lower than 2 days for calibration and 7 days for evaluation. Relative root mean square error for the maximum leaf area index varied from 12.2 to 31.3%; while that for grain yield varied between 3 and 32%. The calibrated model was used to simulate different management scenarios across six sites located in the low latitude, considering 33 growing seasons. Simulations showed a higher yield for 40 pl per m2, as expected, but with greater yield gain increments occurring at low plant density going from 10 to 20 pl per m2. In Santarém, Brazil, MG 8.8 sown on 21 Feb. had a median yield of 2658, 3197, 3442 and 3583 kg/ha, respectively, for 10, 20, 30 and 40 pl per m2, resulting in a relative increase of 20, 8 and 4% for each additional 10 pl per m2. Overall, the crop model had adequate performance, indicating a minimum recommended plant density of 20 pl per m2, while sowing dates and maturity groups showed different yield level and pattern across sites in function of the local climate.

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Section: Discussionsupporting

confidence: 78%

Section: Description Of Plant Density Responsementioning

confidence: 99%

Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

et al. 2020

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“…In the present study, it is hypothesized that the optimal sowing window (OSW) may change according to the ENSO phase (El Niño, La Niña and neutral). This hypothesis has been previously tested and confirmed through simulations using dynamic crop models that integrate meteorological elements, soil characteristics and crop management (Hallinor et al 2018, Lima et al 2019, Nóia Júnior & Sentelhas 2019, and these studies emphasize the potential of crop models as a tool for climate risk analysis.…”

Section: Introductionmentioning

confidence: 59%

“…The ENSO impacts on the Amazon climate, in terms of ecology and hydrology, are well documented (Shimizu et al 2017, Brum et al 2018. However, studies about the ENSO effects on agricultural yield in the region are scarce, even with agricultural losses often evidenced in El Niño years, such as the reduction in soybean yields in the States of northern Brazil in 2015 and 2016 (Nóia Júnior & Sentelhas 2019) One of the methods to minimize the negative effects of climate on the soybean crop is changing the planting date, thus exposing the crop cycle to meteorological conditions favorable to high yields (Lima et al 2019). In the present study, it is hypothesized that the optimal sowing window (OSW) may change according to the ENSO phase (El Niño, La Niña and neutral).…”

Section: Introductionmentioning

confidence: 99%

“…

risk to crop yield (Lima et al 2019), especially in the Amazon, where the vast majority of the agriculture production is under rainfed conditions, and the rainfall pattern is strongly modified by the El Niño-Southern Oscillation (ENSO) phenomenon (Sousa et al 2015).ENSO is a large-scale phenomenon that alters the weather patterns of several places worldwide, 1

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confidence: 99%

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Optimal soybean sowing window adjusted to climatic variability based on El Nino-Southern Oscillation using agrometeorological modeling

Lima

Nunes

Sampaio

et al. 2022

Pesqui. Agropecu. Trop.

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Determining the optimal sowing window (OSW) based on climate variability associated with El Nino-Southern Oscillation (ENSO) can provide valuable information for agricultural planning in the tropics. This study aimed to calibrate, evaluate and apply the CROPGRO-Soybean model for determining the OSW across the ENSO phases for soybean-producing areas in the Pará State, northern Brazil. First, the model was calibrated and evaluated using experimental data collected in the field, between 2006 and 2009. In this process, the model estimates showed a good agreement with the observed data for soybean phenology, growth and yield, demonstrating potential to simulate the crop yield in this part of the Amazon. After calibration, the model was used in the seasonal mode to simulate 18 planting dates, over 39 years and in three locations. The simulated yields were divided into three ENSO phases. The set of sowing dates that showed a high frequency (> 80 %) of yields above 3,500 kg ha−1 integrated the OSW for each location and ENSO phases. The OSW duration differed between locations and ENSO phases, varying more during La Nina than El Nino events. However, regardless of the location or ENSO phase, late sowing was more suitable, because, besides favoring a greater frequency of good climate conditions for the development, growth and high yields, it also favors a lower risk of rainfall during the harvest period.

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Influence of soil management for soybean production under microbial diversity in amazon soils

Cardenas-Alegría

Dantas

Lobato

et al. 2022

Front. Environ. Sci.

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The tropical Amazon has a unique biodiversity that has been affected by the development of pastures and economically important crops, such as soybeans. In the Amazon soil, the communities of microorganisms are diverse and act in different biogeochemical activities relevant to their adaptation to the environment. The assessment of changes in soil microorganism communities is essential to consider the impact of agribusiness action in one of the wealthiest regions in diversity in the world. Thus, the soil microbial diversity of the Amazon forest, the north region of Brazil, was evaluated regarding the influence of soybean farming with regions with periods of two and 14 years of exploitation, with regions of pasture and forest area, through the metagenomics approach with new generation sequencing technology, in addition, it was considered chemical characteristics such as pH value, organic matter content, macronutrients, micronutrients, and cations. High microbial diversity was identified at all collection sites and, despite this, bacterial, archaeal, and virus communities were very diverse between sites, with higher identification of Enterobacter cloacae and species of Pseudomonas, Pseudoplusia includens, Methanosarcina barkeri in the farmed and pasture, whose microbial diversity is influenced by the presence of cations and the interaction of organic matter with clay. It was evident that there is a change in the communities of native microorganisms for others adapted in the areas that had their vegetal cover eliminated.

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Agrometeorological analysis of the soybean potentiality in an Amazonian environment

Cited by 3 publications

References 14 publications

Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

Optimal soybean sowing window adjusted to climatic variability based on El Nino-Southern Oscillation using agrometeorological modeling

Influence of soil management for soybean production under microbial diversity in amazon soils

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