Rit minitab5/31/2023 A significant inverse correlation was recorded between the SL and the yield of both crops (R2 = −0.591 to −0.617 p = 0.01). The FA showed a medium-to-non-significant correlation with the productivity of both alfalfa (R2 = 0.586 p = 0.015) and Rhodes grass (R2 = 0.578 p = 0.01). Statistical analysis revealed that the SL, FA and TWI, which are associated with water distribution, were significantly related to crop yields. Subsequently, on the basis of DEM, the generated elevation, slope and FA model were then compared with the yield and soil chemical properties. Sentinel-2 images were for the creation of yield maps of alfalfa and Rhodes grass. The collected soil samples were analyzed for pH, EC, nitrogen and soil organic carbon. ![]() Topographical parameters of elevation and slope were estimated with the use of a real-time kinematic global positioning system (RTK-GPS), and then the DEM was generated. The soil samples and yield data were obtained from the field inventory. A study was conducted on a 50 ha field to investigate the effect of selected topographic indicators, including elevation (DEM), slope (SL), flow accumulation (FA) and Topographic Wetness Index (TWI) on forage crop production. Understanding the spatial pattern of soil chemical properties along with the topologic indicators is essential for site-specific agriculture management. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of A. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R2 to be 9.3, 7.71, and 0.80, respectively. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R2 to be 6.67, 5.42, and 0.88, respectively. Analyses were carried out in two commercial areas treated with A. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying A. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. To learn how to use key Minitab Statistical Software features, watch one of their quick videos.The application of biological products in agricultural crops has become increasingly prominent. When finished, click the Sign Out link in the upper right-hand side of the window. ![]() Returning users can skip to step 5.įirst time users see a Welcome Screen, click Open Web App and you can skip to step 6.Īs a returning user, click the Minitab Statistical Software, then follow any onscreen prompt to open the web app.įor all users, the Minitab web app launches and you can use the app to create your work. ![]() Note: The Minitab web app does not work with Firefox.Īs a first-time user, review the Subscription Agreement, check the box to accept the terms, then click Accept. Visit Minitab, enter your University Computing Account email address, then click Next. Pitt Information Technology recommends that users start using the web app today. The Minitab web app is available now and can be accessed anytime, anywhere using a browser. In order to receive upgrades, maintenance payments must be received by renewal date noted in the product list. Productĭepartments: Pitt IT will send departments a reminder with renewal costs. Before you use the web app, make sure you have a license. Please consult the following chart for Minitab availability options.
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