Use of discriminant function analysis for forecasting crop yield

The present paper deals with use of discriminant function analysis for developing wheat yield forecast model for Kanpur (India). Discriminant function analysis is a technique of obtaining linear/Quadratic function which discriminates the best among populations and as such, provides qualitative assessment of the probable yield. In this study, quantitative forecasts of yield have been obtained using multiple regression technique taking regressors as weather scores obtained through discriminant function analysis. Time series data of 30 years (1971-2000) have been divided into three categories: congenial, normal and adverse, based on yield distribution. Taking these three groups as three populations, discriminant function analysis has been carried out. Discriminant scores obtained from this have been used as regressors in the modelling. Various strategies of using weekly weather data have been proposed. The models have been used to forecast yield in the subsequent three years 2000-01 to 2002-03 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest.

Maize Yield Forecast Using GIS and Remote Sensing The Case of Kaffa Zone, South Western Ethiopia

Background: Policy makers, government planners and agriculturalist in Ethiopia require accurate and timely information about maize yield and production. Kaffa zone is by far the most important maize producing zone in the country. The manual collection of field data and data processing for crop forecasting by the CSA requires significant amounts of time before official reports are released. Several studies have shown that maize yield can be effectively forecast using satellite remote sensing data. The objectives of this study were to develop a maize yield forecast model in kaffa Zone derived from time series data of eMODIS_NDVI, actual and potential evapotranspiration and CHIRPS for the years 2008-2017.Official grain yield data from the Central statistical Agency of Ethiopia was used to validate the strength of the indices in explaining the yield. Crop masking at crop land area was applied and refined by using agro ecological zones suitable for the crop of interest. Correlation analyses were used to determine associations among crop yield, spectral indices and agro meteorological variables for maize crop of the long rainy season (kiremt). Indices with high correlation with maize yield were identified. Results: Average Normalized Difference Vegetation Index and rainfall have high correlation of maize yield with 84% and 89%, respectively. That means their variables are positively strong related with maize yield. The generated spectro-agro meteorological yield model was successfully tested against the Central Statistical Agency's expected Zone level yields (r2= 0.89, RMSE = 1.54qha1, and 16.7% coefficient of variation).Conclusions: Thus, remote sensing and geographical information system based maize yield forecast improved quality and timelines of the data besides distinguishing yield production levels/areas and making intervention very easy for the decision makers there by proving the clear potential of spectro-agro meteorological factors for maize yield forecasting, particularly for Ethiopia.

Analysing the Accuracy of Crop Yield Prediction Using Deep Learning Algorithm

The Indian farming level decreases step by step inferable from certain components like inordinate usage of pesticides, water level decrement, environment changes, and unpredicted precipitation, and so forth on the farming information, elucidating investigation is performed to comprehend the creation level. The creation of yields isn't expanded inferable from these issues that influences the economy of farming. By utilizing AI strategies, the harvest from given dataset need to foresee by farming areas for forestalling this issue. Yield forecast is of extraordinary importance for yield planning, crop market arranging, crop protection, and gather the executives. Data mining based deep learning is turning out to be progressively significant in crop yield forecast. This strip-mined information will be wont to inform promoting selections, improve sales and abate on prices that has been made in this field by utilizing AI, particularly the Deep Learning (DL) strategy. Profound learning-based models are extensively used to extricate critical yield highlights for forecast. However, these techniques could resolve the yield forecast issue there exist the accompanying insufficiencies: Unable to make a direct non-straight or straight planning between the crude information and harvest yield esteems accuracy; and the exhibition of those models profoundly depends on the nature of the separated elements. Profound deep learning gives guidance and inspiration for the previously mentioned deficiencies. In this paper, I proposed two deep learning models namely ANN and LSTM considering into account the following parameters such as temperature, humidity, pH, rainfall respectively in each model which in turn were compared based on their accuracy level by limiting the blunder and expanding the conjecture accuracy. From my proposed work, I found LSTM is the model that provides us with the better accuracy that that of the ANN. The accuracy of the ANN model is 96.93548387096774 that is approximately 97% and that of the LSTM is 100 % which is obviously the highest. Keywords: Crop yield prediction, Deep learning, Data mining, ANN, LSTM, Accuracy.

Retrieving and processing agro-meteorological data from API-client sources using R software

Objectives The main purpose of this publication is to help users (students, researchers, farmers, advisors, etc.) of weather data with agronomic purposes (e.g. crop yield forecast) to retrieve and process gridded weather data from different Application Programming Interfaces (API client) sources using R software. Data description This publication consists of a code-tutorial developed in R that is part of the data-curation process from numerous research projects carried out by the Ciampitti’s Lab, Department of Agronomy, Kansas State University. We make use of three weather databases for which specific libraries were developed in R language: (i) DAYMET (Thornton et al. in https://daymet.ornl.gov/, 2019; https://github.com/bluegreen-labs/daymetr), (ii) NASA-POWER (Sparks in J Open Source Softw 3:1035, 2018; https://github.com/ropensci/nasapower), and (iii) Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (Funk et al. in Sci Data 2:150066, 2015; https://github.com/ropensci/chirps). The databases offer different weather variables, and vary in terms of spatio-temporal coverage and resolution. The tutorial shows and explain how to retrieve weather data from multiple locations at once using latitude and longitude coordinates. Additionally, it offers the possibility to create relevant variables and summaries that are of agronomic interest such as Shannon Diversity Index (SDI) of precipitation, abundant and well distributed rainfall (AWDR), growing degree days (GDD), crop heat units (CHU), extreme precipitation (EPE) and temperature events (ETE), reference evapotranspiration (ET0), among others.

Forecasting of Kharif Rice and Jute Yield in North Bengal through Statistical Model

Crop yield forecasting under the present climate change scenario needs an effective model and its parameter that how crop respond to the weather variable. A number of weather based models have been developed to estimate the crop yield for the various crops at block, district and state level. Among the different model statistical model is more popular and commonly used. The current study was undertaken to evaluate the performance of statistical model for rice and jute yield forecast of four different district viz. Cooch Behar, Jalpaiguri, Uttar Dinajpurand and Dakhin Dinajpur. Among the four districts Cooch Behar district found superior for kharif rice yield prediction (1.46% error with RMSE 177.68 kg/ha) whereas in case of jute crop its performance was the best in the Jalpaiguri district (-0.44% error with RMSE 217.50 kg/ha).