O estudo da susceptibilidade a erosão laminar é pertinente na mesorregião da Zona da Mata de Minas Gerais, visto a predominância da cobertura de pastagem e pela expressiva degradação do solo. Neste estudo, objetivou-se compreender quais variáveis geodinâmicas são importantes na predição dos processos erosivos laminares e o melhor modelo preditivo entre oito, através de comparações multicritérios, possibilitando entender o fenômeno em uma bacia hidrográfica da mesorregião. Assim, utilizou-se o método de atribuição de notas pela Literatura (L) e Realidade de campo (RC), cuja ponderação de parcela dos processos erosivos (60%) laminares mapeados ponderou a nota das classes das variáveis pela área das mesmas. A integração das variáveis foi por testes de ponderação e integração total e parcial. A avaliação dos modelos gerados foi por estatística descritiva (Box-Plot), diferentes métodos de categorização (Manual, Natural Breaks e Geometrical Interval) e curva ROC com cálculo de eficiência AUC (40% das erosões mapeadas). Os resultados apontaram que a falta umidade é um fator importante para a ocorrência dos processos erosivos laminares, por outro lado, as variáveis morfométricas não foram importantes para a predição. Modelos baseados na RC (72,41% AUC médio) obteve eficiência consideravelmente maior do que a L (65,41% AUC médio), já quando comparado a integração de todas as variáveis geodinâmicas e somente as mais importantes e quando integrado com ponderação e sem ponderação, não houve considerável diferença estatística. O modelo mais eficiente obteve 76,3% AUC, considerado boa e estava adequado a realidade da área estudada. Study of Susceptibility to Sheet Erosion in a Watershed in Zona da Mata, Minas Gerais, BrazilABSTRACTThe study of susceptibility to surface erosion is relevant in the mesoregion of the Zona da Mata of Minas Gerais, given the predominance of pasture cover, the significant degradation of the soil and the stagnation of the agricultural sector. In this study, the objective was to understand which geodynamic variables are important in the prediction of surface erosive processes and the best predictive model among eight, through multicriteria comparisons, making it possible to understand the phenomenon in a watershed in the mesoregion. Thus, it was used the method of attributing grades by Literature (L) and Field Reality (RC), whose weighting of the mapped surface erosive (60%) processes weighted the grade of the variable classes by their area. The integration of the variables was through weighting tests and total and partial integration. The evaluation of the models generated was by descriptive statistics (Box-Plot), different methods of categorization (Manual, Natural Breaks and Geometrical Interval) and ROC curve with AUC efficiency calculation (40% of the mapped erosions). The results showed that the lack of moisture is an important factor for the occurrence of surface erosive processes, on the other hand, the morphometric variables were not important for the prediction. Models based on RC (72.41% average AUC) achieved considerably greater efficiency than L (65.41% average AUC), when compared to the integration of all geodynamic variables and only the most important ones and when integrated with weighting and without weighting, there was no considerable statistical difference. The most efficient model obtained 76.3% AUC, considered good and was adequate to the reality of the studied area.Key words: Geotechnologies; Comparison of Risk Models; Multicriteria Analysis
In order to assist in high-yield agricultural management in multiple cropping systems, it is essential to understand the link between the rainy season onset and crops sowing dates, since it considerably affects the management, yield and output. We built crop calendars derived from remote sensing products and investigated the link between sowing dates and the onset of the rainy season in irrigated and rainfed agriculture in Western Bahia, a new and important agricultural frontier in Brazilian Cerrado. Crop sowing dates were obtained from green-up dates from 2001 to 2019. Rainy season onset dates were determined using CHIRPS daily precipitation data. Results indicate that sowing occurs from 26 October to 15 November and the rainy season starts from 17 to 27 October. Rainfed sowing dates are strongly correlated to rainy season onset and are particularly affected in years where rains are delayed. Sowing dates in irrigated pixels occur up to 25 days earlier than rainfed and are not correlated to rainy season onset. Irrigated farms are sowing earlier and in a shorter window than rainfed, with a stronger resilience in years where rains are delayed, and have adapted their sowing operation towards a more intensive agriculture and efficient water use during the rainy season.
Na cafeicultura, as intempéries afetam não somente as plantas, mas também as mulheres que atuam no processo produtivo dessa commodity. Logo, é necessário que as mulheres que trabalham na produção do café tenham maior conhecimento acerca das influências das Mudanças Climáticas na cafeicultura. Objetivou-se analisar a percepção das mulheres que trabalham na cafeicultura em 15 municípios localizados na região das Matas de Minas sobre a influência das mudanças climáticas na produção de café. Foram aplicados 67 questionários semiestruturados às mulheres em 15 municípios da região das Matas de Minas, os dados foram tabulados e foi identificado que a maior parte delas têm a televisão como principal fonte de informação sobre as mudanças climáticas globais e acreditam que tais mudanças não irão contribuir para a melhoria do café na região bem como podem influenciar na extinção da cafeicultura da região no futuro, interferindo assim, de alguma forma no futuro delas. De modo geral as mulheres que declararam ter conhecimento médio sobre mudanças climáticas e aquecimento global.
Robust monitoring techniques for perennial crops have become increasingly possible due to technological advances in the area of Remote Sensing (RS), and the products are available through the European Space Agency (ESA) initiative. RS data provides valuable opportunities for detailed assessments of crop conditions at plot level using high spatial, spectral, and temporal resolution. This study addresses the monitoring of coffee at the plot level using RS, analyzing the relationship between the spatio-temporal variability of the Leaf Area Index (LAI) and the crop coefficient (Kc); the Kc being a biophysical variable that integrates the potential hydrological characteristics of an agroecosystem compared to the reference crop. Daily and one-year Kc were estimated using the relation of crop evapotranspiration and reference. ESA Sentinel-2 images were pre-analyzed and atmospherically corrected, and Top-of-the-Atmosphere (TOA) reflections converted to Top-of-the-Canopy (TOC) reflectance. The TOCs resampled at the 10m resolution, and with the angles corresponding to the directional information at the time of the acquisition, the LAI was estimated using the trained neural network available in the Sentinel Application Platform (SNAP). During 75% of the monitored days, Kc ranged between 1.2 and 1.3 and, the LAI analyzed showed high spatial and temporal variability at the plot level. Based on the relationship between the biophysical variables, the LAI variable can substitute the Kc and be used to monitor the water conditions at the production area as well as analyze spatial variability inside that area. Sentinel-2 products could be more useful in monitoring coffee in the farm production area.
Created in 2014, the Serra da Gandarela National Park (SGNP), is repeatedly affected by wildfires. This Conservation Unit is located in the Iron Quadrangle (MG), in a transition zone between the Cerrado and the Atlantic Forest biomes, and is characterized by a complex mosaic of phytophysiognomies. The aim of this investigation was to compare the performance of two risk mapping models for forest fire in the SGNP and its surroundings, based on two different approaches, being one by multicriteria analysis, AHP method and the other a simple probability method, called Hot Spot History, which provided information on the areas of highest and lowest risk and their environmental and human characteristics. Spatial data from remote sensing and GIS were used to simulate, in sequence, the fire ignition, the fire spread and, finally, the risk of wildfire. The validation of the risk models was performed by the Kappa coefficient (K). The results showed that the model based on the History of Hot Points obtained greater accuracy (0.61) than the model generated by the AHP method (0.54). The Brazilian Savanna, Rupestrian Fields and Field coverings were the most susceptible to wildfire, as they are formed by herbaceous vegetations and are located very close to urban agglomerations and roads. The slopes oriented to the North and West were important for the prediction of wildfires slope and, on the other hand, the slope of the terrain was not important to discretize the areas of greater and lesser fragility to the referred ecological disturbance.
All the characteristics of the mountainous environment directly influence the coffee crops, and subsequently, on the final coffee note, that reflects the quality of the beverage produced in a region. Despite increasing coffee research, little is known about the influence of the water indices, factors, and the elements of climate on top-quality coffee production potential. Thus, the present study was carried out aiming to analyze the water indices, causes, and aspects of clime, to identify those that most contribute to the potential production of high-quality Arabica coffee beverages in a mountain environment. We considered harvesting the coffee fruits at the cherry stage in 26 municipalities in the Matas de Minas region in the Atlantic Forest Biome in the eastern state of Minas Gerais, and the International Cup of Excellence method was adopted for the sensory evaluation. The principal components analysis and the multiple linear regression (MLR) were used to relate the local environmental variables with the final grade of the coffee beverage. As a result, the Multiple Linear Regression model showed the value of 0.63 for R2. This result means that the joint variability of all the variables considered explained 63% of the changes in coffee beverage quality. And the altitude impact on the grade achieved for the coffee beverage produced in the Matas de Minas region, represented by β, was 0.008068, meaning that for every 100 meters of increase in the altitude, there is an approximate increment of 0.8 points in the final note achieved for the coffee beverage. Among all the environmental characteristics studied, the climatic factor altitude was the main contributor to the coffee top-quality production potential in the Matas de Minas region.
Coffee is among the most significant products in Brazil. Minas Gerais is the largest state producer of Arabica coffee. Coffee activity has excellent growth potential, which justifies the identification of new areas for expansion of the culture. This study aimed to determine factors that affect the spatial distribution of coffee plantations the most, as well as to identify areas with a greater aptitude for its expansion in the region of the Matas de Minas (63 municipalities). The MaxEnt software was used to elaborate a model capable of describing the area with the highest potential for estimating the probability of coffee adequacy. The elaboration of the model considered the records of occurrence, climatic and topographic variables of Matas de Minas, the second largest state producing region. The area under the curve (AUC), the omission rate and the Jackknife test were used for validation and analysis of the model. The model was accurate with an AUC of 0.816 and omission rate of 0.54% for the ‘test’. It was identified that the potential distribution of coffee in Matas de Minas is determined by changes in the annual maximum temperature, although it did not generate a significant gain when omitted, accounting for a considerable loss in the model. However, the most influential variables on the delineation of distribution were, the altitude and the annual average temperature. The most favorable areas for expansion of coffee culture in the Matas de Minas were found in the vicinity of the region of Alto Caparaó.Abbreviations used: A1 (altitude); A2 (maximum annual temperature); A3 (annual minimum temperature); BIO 1 (annual average temperature 1); BIO 4 (temperature seasonality), BIO 12 (annual precipitation); BIO 15 (precipitation seasonality); csv (comma-separated values); AUC (area under the curve).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.