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Statistical yielding models of some irrigated vegetable crops in dependence on water use and heat supply

Vozhehova Raisa, Kokovikhin Sergii, Lykhovyd Pavlo V., Balashova Halyna, Lavrynenko Yuriy, Biliaieva Iryna, Markovska Olena

Journal of Water and Land Development

2020

no. 45

190--197

Statistical analysis is helpful for better understanding of the processes which take place in agricultural ecosystems. Particular attention should be paid to the processes of crops’ productivity formation under the influence of natural and anthropogenic factors. The goal of our study was to provide new theoretical knowledge about the dependence of vegetable crops’ productivity on water supply and heat income. The study was conducted in the irrigated conditions of the semi-arid cold Steppe zone on the fields of the Institute of Irrigated Agriculture of NAAS, Kherson, Ukraine. We studied the historical data of productivity of three most common in the region vegetable crops: potato, tomato, onion. The crops were cultivated by using the generally accepted in the region agrotechnology. Historical yielding and meteorological data of the period 1990–2016 were used to develop the models of the vegetable crops’ productivity. We used two approaches: development of pair linear models in three categories (“yield – water use”, “yield – sum of the effective air temperatures above 10°C”); development of complex linear regression models taking into account such factors as total water use, and temperature regime during the crops’ vegetation. Pair linear models of the crops’ productivity showed that the highest effect on the yields of potato and onion has the water use index (R2 of 0.9350 and 0.9689, respectively), and on the yield of tomato – temperature regime (R2 of 0.9573). The results of pair analysis were proved by the multiple regression analysis that revealed the same tendencies in the crop yield formation depending on the studied factors.

Spring Row Crops Productivity Prediction Using Normalized Difference Vegetation Index

Vozhehova Raisa, Maliarchuk Mykola, Biliaieva Iryna, Lykhovyd Pavlo, Maliarchuk Anastasiia, Tomnytskyi Anatoliy

Journal of Ecological Engineering

2020

Vol. 21, nr 6

176--182

The results of statistical modelling for the yields prediction of spring row crops, namely, maize, sorghum and soybean, depending on the values of the remotely sensed normalized difference vegetation index (NDVI) at critical stages of the crops growth and development were presented. The spatial NDVI data obtained from the Sentinel-2 satellite were used to create the models. Quadratic regression analysis was applied to develop the yielding models based on true yield data of the crops obtained in the period of 2017 and 2018 at the experimental field of the Institute of Irrigated Agriculture of NAAS, Ukraine. The results of statistical modelling revealed that the method is suitable for precise yield prediction, and the best stages for NDVI screening and use in this purpose are different for the studied crops. The best accuracy of prediction could be obtained at the stage of tasselling (VT) or silking (R1) for maize (the mean absolute percentage error MAPE is 8.75%); at the stage of second trifoliate (V2) for soybean (MAPE is 3.75%), and at the stage of half bloom (S6) for sorghum (MAPE is 17.62%). The yield predictions by NDVI are reliable at a probability level of 95% (p < 0.05).