Geographical patterns in climate and agricultural technology drive soybean productivity in Brazil

Gaps and opportunities in modelling human influence on species distributions in the Anthropocene

Understanding species distributions is a global priority for mitigating environmental pressures from human activities. Ample studies have identified key environmental (climate and habitat) predictors and the spatial scales at which they influence species distributions. However, regarding human influence, such understandings are largely lacking. Here, to advance knowledge concerning human influence on species distributions, we systematically reviewed species distribution modelling (SDM) articles and assessed current modelling efforts. We searched 12,854 articles and found only 1,429 articles using human predictors within SDMs. Collectively, these studies of >58,000 species used 2,307 unique human predictors, suggesting that in contrast to environmental predictors, there is no 'rule of thumb' for human predictor selection in SDMs. The number of human predictors used across studies also varied (usually one to four per study). Moreover, nearly half the articles projecting to future climates held human predictors constant over time, risking false optimism about the effects of human activities compared with climate change. Advances in using human predictors in SDMs are paramount for accurately informing and advancing policy, conservation, management and ecology. We show considerable gaps in including human predictors to understand current and future species distributions in the Anthropocene, opening opportunities for new inquiries. We pose 15 questions to advance ecological theory, methods and real-world applications.

Scientometric Analysis for Spatial Autocorrelation-Related Research from 1991 to 2021

Spatial autocorrelation describes the interdependent relationship between the realizations or observations of a variable that is distributed across a geographical landscape, which may be divided into different units/areas according to natural or political boundaries. Researchers of Geographical Information Science (GIS) always consider spatial autocorrelation. However, spatial autocorrelation research covers a wide range of disciplines, not only GIS, but spatial econometrics, ecology, biology, etc. Since spatial autocorrelation relates to multiple disciplines, it is difficult gain a wide breadth of knowledge on all its applications, which is very important for beginners to start their research as well as for experienced scholars to consider new perspectives in their works. Scientometric analyses are conducted in this paper to achieve this end. Specifically, we employ scientometrc indicators and scientometric network mapping techniques to discover influential journals, countries, institutions, and research communities; key topics and papers; and research development and trends. The conclusions are: (1) journals categorized into ecological and biological domains constitute the majority of TOP journals;(2) northern American countries, European countries, Australia, Brazil, and China contribute the most to spatial autocorrelation-related research; (3) eleven research communities consisting of three geographical communities and eight communities of other domains were detected; (4) hot topics include spatial autocorrelation analysis for molecular data, biodiversity, spatial heterogeneity, and variability, and problems that have emerged in the rapid development of China; and (5) spatial statistics-based approaches and more intensive problem-oriented applications are, and still will be, the trend of spatial autocorrelation-related research. We also refine the results from a geographer’s perspective at the end of this paper.

Modeling the Impact of Climatological Factors and Technological Revolution on Soybean Yield: Evidence from 13-Major Provinces of China

In recent years, the changing climate has become a major global concern, and it poses a higher threat to the agricultural sector around the world. Consequently, this study examines the impact of changing climate and technological progress on soybean yield in the 13 major provinces of China, and considers the role of agricultural credit, farming size, public investment, and power of agricultural machinery from 2000 to 2020. Fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied to assess the long-run effect, while Dumitrescu and Hurlin's (2012) causality test is used to explore the short-run causalities among the studied variables. The results revealed that an increase in the annual mean temperature negatively and significantly affects soybean yield, while precipitation expressively helps augment soybean yield. Furthermore, technological factors such as chemical fertilizers accelerate soybean yield significantly, whereas pesticides negatively influence soybean yield. In addition, farming size, public investment, and power of agricultural machinery contribute remarkably to soybean yield. The causality results endorse that chemical fertilizers, pesticides used, agricultural credit, public investment, and power of agricultural machinery have bidirectional causality links with soybean yield. This study suggests several fruitful policy implications for sustainable soybean production in China.

Molecular Characterisation of Soybean Osmotins and Their Involvement in Drought Stress Response

Osmotins are multifunctional proteins belonging to the thaumatin-like family related to plant stress responses. To better understand the functions of soybean osmotins in drought stress response, the current study presents the characterisation of four previously described proteins and a novel putative soybean osmotin (GmOLPa-like). Gene and protein structure as well as gene expression analyses were conducted on different tissues and developmental stages of two soybean cultivars with varying dehydration sensitivities (BR16 and EMB48 are highly and slightly sensitive, respectively). The analysed osmotin sequences share the conserved amino acid signature and 3D structure of the thaumatin-like family. Some differences were observed in the conserved regions of protein sequences and in the electrostatic surface potential. P21-like present the most similar electrostatic potential to osmotins previously characterised as promoters of drought tolerance in Nicotiana tabacum and Solanum nigrum. Gene expression analysis indicated that soybean osmotins were differentially expressed in different organs (leaves and roots), developmental stages (R1 and V3), and cultivars in response to dehydration. In addition, under dehydration conditions, the highest level of gene expression was detected for GmOLPa-like and P21-like osmotins in the leaves and roots, respectively, of the less drought sensitive cultivar. Altogether, the results suggest an involvement of these genes in drought stress tolerance.

Historical Changes in Land Use and Suitability for Future Agriculture Expansion in Western Bahia, Brazil

Western Bahia is a critical region in Brazil’s recent expansion of agricultural output. Its outstanding increase in production is associated with strong growth in cropland area and irrigation. Here we present analyses of Western Bahian historical changes in land use, including irrigated area, and suitability for future agricultural expansion that respects permanent protection areas and the limits established by the Brazilian Forest Code in the Cerrado biome. For this purpose, we developed a land use and land cover classification database using a random forest classifier and Landsat images. A spatial multicriteria decision analysis to evaluate land suitability was performed by combining this database with precipitation and slope data. We demonstrate that between 1990 and 2020, the region’s total agricultural area increased by 3.17 Mha and the irrigated area increased by 193,480 ha. Throughout the region, the transition between the different classes of land use and land cover followed different pathways and was strongly influenced by land suitability and also appears to be influenced by Brazil’s new Forest Code of 2012. We conclude that even if conservation restrictions are considered, agricultural area could nearly double in the region, with expansion possible mostly in areas we classify as moderately suitable for agriculture, which are subject to climate hazards when used for rainfed crops but are otherwise fine for pastures and irrigated croplands.

Evaluating the Contribution of Soybean Rust- Resistant Cultivars to Soybean Production and the Soybean Market in Brazil: A Supply and Demand Model Analysis

Soybean rust (SBR), caused by Phakopsora pachyrhizi (Sydow & Sydow), has become a serious issue in Brazil. As Brazil is one of the largest soybean-producing and exporting countries in the world, a considerable decrease in soybean production due to SBR would have a significant impact on the global soybean market. SBR-resistant cultivars have been developed to prevent a decrease in soybean production. This study was conducted to evaluate the effect of SBR-resistant cultivars on soybean production and the soybean market in Brazil using a supply and demand model. This model consists of functions of yield, cultivated area, exports, and stock changes of soybean and soybean products, demand for soybean products, and price linkages. Five scenarios were simulated to evaluate the economic impact of adopting SBR-resistant cultivars as follows: One without SBR infection, two with serious production losses due to SBR in the south and southeast regions and all the states of Brazil, and two with the adoption of SBR-resistant cultivars in the south and south-east regions and all the states of Brazil. Our simulations suggest that adopting SBR-resistant cultivars reduces the cost of controlling SBR by approximately half and is essential for sustainable soybean production and a stable global soybean market.

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Precisely estimating the yield of paddy rice is crucial for national food security and development evaluation. Rice yield estimation based on satellite imagery is usually performed with global regression models; however, estimation errors may occur because the spatial variation is not considered. Therefore, this study proposed an approach estimating paddy rice yield based on global and local regression models. In our study area, the overall per-field data might not available because it took lots of time and manpower as well as resources. Therefore, we gathered and accumulated 26 to 63 ground survey sample fields, accounting for about 0.05% of the total cultivated areas, as the training samples for our regression models. To demonstrate whether the spatial autocorrelation or spatial heterogeneity exists and dominates the estimation, global models including the ordinary least squares (OLS), support vector regression (SVR), and the local model geographically weighted regression (GWR) were used to build the yield estimation models. We obtained the representative independent variables, including 4 original bands, 11 vegetation indices, and 32 texture indices, from SPOT-7 multispectral satellite imagery. To determine the optimal variable combination, feature selection based on the Pearson correlation was used for all of the regression models. The case study in Central Taiwan rendered that the error rate was between 0.06% and 13.22%. Through feature selection, the GWR model’s performance was more relatively stable than the OLS model and nonlinear SVR model for yield estimation. Where the GWR model considers the spatial autocorrelation and spatial heterogeneity of the relationships between the yield and the independent variables, the OLS and nonlinear SVR models lack this feature; this led to the rice yield estimation of GWR in this study be more stable than those of the other two models.