Knowledge of the spatial variability of soil properties and of forage yield is needed for informed use of soil inputs such as variable rate technology (VRT) for lime and fertilizers. The objective of this research was to map and evaluate the spatial variability of soil properties, yield, lime and fertilizer needs and economic return of an alfalfa pasture. The study was conducted in a 5.3 ha irrigated alfalfa pasture in Sao Carlos, SP, Brazil that was directly grazed and intensively managed in a 270-paddock rotational system. Alfalfa shoot dry matter yield was evaluated before grazing. Soil samples were collected at 0-0.2 m depth, and each sample represented a group of 2 or 3 paddocks. Apparent soil electrical conductivity (ECa) was measured with a contact sensor. The cost of producing 1 ha of alfalfa was estimated from the amount of lime and fertilizer needed and was then used to estimate the total cost of production for the dairy system. The alfalfa dry matter yield was used to simulate the pasture stocking rate, milk yield, gross revenue and net profit. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms with VESPER software, the soil fertility information and economic return were modeled with SPRING software. The results showed that geostatistics and GIS were effective tools for revealing soil and pasture spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application maps. Spatial variation in forage and spatial estimates of stocking and milk yield are adequate pasture management tools. Spatial analyses of needs, forage availability and economic return are management tools for avoiding economic problems, as well as potential environmental problems, caused by unbalanced nutrient supplies and over-or under-grazing.
Projected change in forage production under a range of climate scenarios is important for the evaluation of the impacts of global climate change on pasture-based livestock production systems in Brazil. We evaluated the effects of regional climate trends on Panicum maximum cv. Tanzânia production, predicted by an agrometeorological model considering the sum of degree days and corrected by a water availability index. Data from Brazilian weather stations were considered as the current climate (baseline), and future scenarios, based on contrasting scenarios in terms of increased temperature and atmospheric CO 2 concentrations (high and low increases), were determined for 2013-2040 (2025 scenario) and for 2043-2070 (2055 scenario). Predicted baseline scenarios indicated that there are regional and seasonal variations in P. maximum production related to variation in temperature and water availability during the year. Production was lower in the Northeast region and higher in the rainforest area. Total annual production under future climate scenarios was predicted to increase by up to 20% for most of the Brazilian area, mainly due to temperature increase, according to each climate model and scenario evaluated. The highest increase in forage production is expected to be in the South, Southeast and Central-west areas of Brazil. In these regions, future climate scenarios will not lead to changes in the seasonal production, with larger increases in productivity during the summer. Climate risk is expected to decrease, as the probability of occurrence of low forage productions will be lower. Due to the predicted increase in temperature and decrease in rainfall in the Northeast area, P. maximum production is expected to decrease, mainly when considering scenarios based on the PRECIS model for the 2055 scenario.
Knowledge on spatial variability of soil properties is useful for the rational use of inputs, as in the site specific application of lime and fertilizer. Crop-livestock-forest integrated systems (CLFIS) provide a strategy of sustainable agricultural production which integrates annual crops, trees and livestock activities on a same area and in the same season. Since the lime and fertilizer are key factors for the intensification of agricultural systems in acid-soil in the tropics, precision agriculture (PA) is the tool to improve the efficiency of use of these issues. The objective of this research was to map and evaluate the spatial variability of soil properties, liming and fertilizer need of a CLFIS. The field study was carried out in a 30 ha area at Embrapa Pecuária Sudeste in São Carlos, SP, Brazil. Soil samples were collected at 0–0.2 m depth, and each sample represented a paddock. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms, the soil fertility information were modeled. Spatial variability soil properties and site specific liming and fertilizer need were modeled by kriging and inverse distance weighting (IDW) techniques. Another approach used was based on lime and fertilizer recommendation considering the paddocks as the minimum management unit. The results showed that geostatistics and GIS were useful tools for revealing soil spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application zones. Spatial analyses of crop needs and requirement can provide management tools for avoiding potential environmental problems, caused by unbalanced nutrient supplies.
RESUMOO presente trabalho objetivou aplicar os conceitos de geoestatística e geoprocessamento para a obtenção de zonas de manejo de uma área de pastagem de capim Tanzânia, em São Carlos -SP, e delimitação de unidades de manejo para aplicação de calagem e adubação, com base no melhor método de interpolação. Com os resultados de análise de solo foram realizadas análises geoestatísticas para avaliação da dependência espacial dos atributos químicos. Os mapas foram obtidos pelo método de interpolação por Krigagem Ordinária e a definição das zonas de manejo foi realizada por meio de lógica fuzzy. A partir dos mapas dos parâmetros químico do solo gerou-se o mapa de zonas de manejo resultando em cinco zonas sendo: 0,02ha (1,2% da área total) consideradas como "muito baixa" fertilidade; 0,3ha (18%) "baixa" fertilidade; 0,75ha (44%) como "média" fertilidade; 0,55ha (32%) como "alta" fertilidade e, 0,08ha (4,8%) como "muita alta" fertilidade. A comparação dos métodos de interpolação demonstrou que a Krigagem Ordinária foi a melhor metodologia para o estudo. A geoestatística e o geoprocessamento demonstraram ser técnicas que auxiliam nas decisões estratégicas e complexas em relação ao gerenciamento do sistema de produção agrícola. Palavras-chave: agricultura de precisão, zonas de manejo, métodos de interpolação GEOSTATISTICS AND GIS IN THE DECISION MAKING OF THE USE OF INPUTS IN A PASTURE ABSTRACTThe present study aimed to apply the concepts of geostatistics and GIS to obtain management zones of a pasture Tanzania grass in São Carlos -SP/Brazil, and delimitation of management units for the application of liming and fertilization, based on the best interpolation method. Geostatistical analysis were performed based on results of soil analysis in order to evaluate the spatial dependence of the chemical attributes. The maps were obtained by Ordinary Kriging interpolation method and the definition of management zones was performed by fuzzy logic. From the maps of chemical parameters of the soil has resulted from the management zone map, resulting in five areas being: 0.02 ha (1.2% of total area) regarded as "very low" fertility; and 0.3 ha (18%) "low" fertility; 0.75 ha (44%) as "average" fertility; 0.55 ha (32%) * karoline.eduarda@usp.br
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