Precision Agriculture
Alireza Dahmardeh; Ali Shahriari; Mohammad reza Pahlavan Rad; Asma Shabani; MARYAM GHOEBANI
Abstract
Introduction Crop yield modeling is an important part of ecological modeling because it makes possible plant production prediction and increase understanding of how it works. In other words, plant and crop growth simulation and yield modeling are mathematical expressions of plant growth stages and processes ...
Read More
Introduction Crop yield modeling is an important part of ecological modeling because it makes possible plant production prediction and increase understanding of how it works. In other words, plant and crop growth simulation and yield modeling are mathematical expressions of plant growth stages and processes under the influence of environmental and managerial factors. Wheat is one of the key crops grown worldwide and is a source of nourishment for millions of people around the world. Therefore, studying this strategic crop is very importance. On the other hand, more than 70% of wheat and 84% of barley in Sistan and Baluchestan province were produced in Sistan plain and wheat has the highest area under cultivation among different crops, in this arid region. So, the aim of this study was modeling wheat yield with some soil characteristics and determination of the most important soil factors affecting wheat yield in the Sistan plain.Materials and Methods This research was done in the educational and research farm of University of Zabol. Topsoil (0-30 cm) sampling of 100 soil sample was done randomly. Clay, silt, sand abundances and soil texture class, soil pH, electrical conductivity, apparent electrical conductivity of soil, organic carbon, phosphorus, potassium and nitrogen were measured by conventional methods. Wheat plant samples were taken from a one m2 plot and the grain weight, 1000-grain weight and total weight were measured. Performance modeling was performed by three methods of multi-linear regression (MLR), multi-layer perceptron (MLP) and support vector machines (SVMs) by two kernels types linear(SVM-L) and radial basic function (SVM-RBF). It should be noted, before modeling, 80% of the data were selected for modeling (or training) and 20% for testing (or validation) of the models. These data (training and validation) were the same for all models. Coefficient of determination (R2) and the root mean square error (RMSE) were the criteria for comparing the models. Sensitivity analysis was used to determine the most important soil factors affecting wheat yield.Results and Discussion The results of soil properties analyses showed that the soil of this area is non-saline and alkaline soil, has a medium to coarse soil texture and the soil fertility conditions are poor to moderate. The results of comparing the models showed that the highest R2 and the lowest RMSE in estimating all three wheat yield indices were related to the MLP method (grain weight with R2= 0.61, 1000-grain weight with R2= 0.64 and total yield with R2= 0.76). After MLP, with less difference, the SVMs method with two kernels types of linear (grain weight with R2= 0.54, 1000-grain weight with R2= 0.44 and total yield with R2= 0.65) and radial basic function (grain weight with R2= 0.48, 1000-grain weight with R2= 0.58 and total yield with R2= 0.67) showed the better modeling and finally the MLR (grain weight with R2= 0.20, 1000-grain weight with R2= 0.27 and total yield with R2= 0.40) showed the lowest accuracy in modeling the yield components of wheat. The results of sensitivity analysis of wheat yield components showed that total soil nitrogen, clay, silt and soil organic matter had the highest on wheat yield components (grain weight: nitrogen, clay and organic matter; 1000-grain weight: nitrogen, silt and clay; and total yield: clay, organic matter and nitrogen) and soil pH had the least effect on it, maybe because of its low variation.Conclusion Due to harsh environmental conditions in the arid regions, the study of crops yield is very important for the optimal management of facilities and resources. Investigating the application of several wheat yield modeling methods using some soil characteristics in the arid region of Sistan showed that the perceptron neural network (MLP) performed better in predicting the yield components of wheat than other models. Also, some chemical and physical properties of soil that affect the soil fertility and water storage conditions in the soil (soil nitrogen, organic matter, clay and silt contents), were the most affecting factors on the yield of wheat in this arid region. It is important to note that attention to other soil properties as well as climatic parameters and studies and monitoring wheat yield for several years can can lead to more accurate modeling of this strategic crop and thus optimal farm management.
F. Torkamani; H. Piri Sahragard; M.R. Pahlavan Rad; M. Nohtani
Abstract
Introduction Spatial variations of soil properties is a natural event, which recognizing these changes is inevitable in order to planning and right management of both agricultural and natural resources. Soil organic carbon (SOC) is the most important factor in soil fertility and quality, climate change ...
Read More
Introduction Spatial variations of soil properties is a natural event, which recognizing these changes is inevitable in order to planning and right management of both agricultural and natural resources. Soil organic carbon (SOC) is the most important factor in soil fertility and quality, climate change and reduction of greenhouse gas emissions. Furthermore, evaluating the rates and spatial distribution of the soil properties, land improvement and restoration can be traced from the carbon sequestration index. According to the above, providing quantitative and qualitative conservation of soil properties such as SOC can be considered an effective way to achieve sustainable development of natural and environmental resources. Digital soil mapping (DSM) can determine the spatial variations of soil organic carbon by exploring the relationship between soil properties and effective environmental variables. Different statistical models such as regression trees and random forest are used in order to communicate between soil characteristics and its spatial distribution. The present study was carried out to investigate the spatial distribution of SOC, as well as, to determine the most important variables affecting their prediction in Ravang watershed in Minab County. Materials and MethodsRavang watershed with an area of 13821.6 hectares is located in Hormozgan province, Minab city. The maximum and minimum elevations are 357 and 33 meters, respectively. Digital Elevation Model of Ravang watershed was used to extract 17 environmental covariates (such as elevation, aspect, slope, valley depth,…) by SAGA software (http://www.gdem.aster.ersdac.or). Moreover, two environmental covariates related to remote sensing including Normalized Difference Salinity Index (NDSI) and Normalized Difference Vegetation Index (NDVI) were determined in the study area. In addition, the maps of land use, sand, silt, clay and pH were used as covariates in modeling. In order to determining the location of sampling points, the conditioned hyper-cube technique was used. After determining of soil sample location, field sampling was carried out at a depth of 0-30 cm. then, 100 soil samples were taken and the amount of SOC was measured. Random forest model was applied to the relationship between SOC and covariates. The model includes two user-defined parameters, including the number of variables used in the construction of each tree, which expresses the power of each independent tree and the number of trees in each forest. Considering the strength of independent trees, the predictive accuracy of the model increases, conversely, the correlation between them will decrease. The accuracy of the soil organic carbon distribution was also evaluated using root mean square error (RMSE), mean error (ME) and correlation coefficient (R2), which were determined. Results and Discussion Based on the present study results, elevation, soil silt and sand maps, channel network base level, slope and NDVI are the most important factors on predicting the of SOC variations. The results indicated that RMSE, ME and R2 were 0.36, 0.26 and 0.38, respectively .Results also showed that affecting erosion and sediment, as well as, human effect, have the most impact on the SOC soil spatial distribution in the Ravang watershed. Moreover, result show SOC deficiency in the soil of Ravang watershed due to high salinity, low percent of vegetation cover and land use changes. In addition, drought intensifying and decrease in precipitation have reduced SOC content, which itself causes changes in the texture and chemical properties of the soil and, as a consequence, makes them more susceptible to erosion. Conclusion The variability of SOC is very high in the study area because of intensive water erosion and land use change. Overall, the results of the present study indicated that the critical condition of soil organic carbon in the Ravang watershed, which requires a comprehensive management of the region's water and soil resources to improve soil conditions and increase the reserves of this important and influential variable in the soil structure. On the other hand, despite of the acceptable performance of the random forest model in estimating of soil properties, due to high variability of some soil properties, model prediction performance may be decreased.
