The Use of Spatial Normalized Difference Vegetation Index for Determination of Humus Content in the Soils of Southern Ukraine

Spatial normalized difference vegetation index finds various applications in crop monitoring and prediction. Although this index is mainly aimed to represent the state of vegetation cover, it is suggested that it could be utilized for other remote monitoring purposes, for example, soil humus content monitoring. The study was carried out in 2022-2023 fallow-field period in Kherson oblast, the South of Ukraine, to establish the relationship between the values of bare-soil normalized difference vegetation index and content of humus in the soils of the region. Statistical modeling was performed using the best subsets regression analysis in BioStat v.7 and artificial neural network with back propagation of error algorithm in Tiberius XL. The best performance was recorded for the combined model of cubic regression and artificial neural network, with moderate fitting quality (coefficient of determination is 0.29), and good prediction accuracy (mean average percentage error is 13.22%). The results approve the suggestion of possibility of spatial vegetation index use in soil state monitoring, especially, if further scientific work enhances the fitting quality of the model

A Life Factor Approach to the Yield Prediction: a Comparison with a Technological Approach in Reliability and Accuracy

There is a number of various approaches to the development of yield predictive models in agriculture. One of the most popular is based on the yield modeling from parameters of crop cultivation technology. However, there is another view on the yield prediction models, particularly, based on the use of life factors as yielding parameters. Our study is devoted to the comparison of a conventional technological approach to the yield prediction with a less prevalent approach of life factor based yield modeling. Testing of two approaches was performed by using the yielding data of sweet corn cultivated in the field trials in the drip-irrigated conditions of the South of Ukraine under the different technological treatments, viz. plowing depth, nutrition, and crop density. We developed two multiple linear regression models to compare their efficiency in the yielding predictions. One of the models used cultivation technology parameters as the inputs while another used life factors as the inputs. Life factors were expressed in numeric values by using such a converter: total water consumption of the crop was used as the factor of water, the total sum of positive temperatures was used as the factor of heat, and the total sum of the main nutrients (NPK) available in the soil was used as the factor of nutrition. The results of the study proved an equal accuracy and reliability of the studied models of sweet corn yields, which is obvious from the values of RSQ. RSQ of the both studied regression models was 0.897. However, additional check of the modeling approaches applied in the feed-forward artificial neural network showed that the life factor based model with the RSQ value of 0.953 provided better yield predictions than the technologically based model with the RSQ value of 0.913. So, we concluded that the life factor approach should be preferred to the technological approach in the development of yield predictive models for agriculture

Agro-Environmental Evaluation of Irrigation Water from Different Sources, Together with Drainage and Escape Water of Rice Irrigation Systems, According to its Impact on Maize

Water supply deficit requires agro-environmental rationale for the use of alternative water sources to feed agricultural crops, viz.: industrial wastes, municipal drains, farm animal waste, drainage and escape water of rice irrigation systems. We have analyzed the quality of irrigation water from different sources, with regard to the content of cations, anions, water-soluble salts, power of hydrogen (рН), sodium adsorption ratio (SAR), etc. in it. In the course of greenhouse trial, we diagnosed its impact on the indicator crop (maize) (Zea mays L.) with its herbage crop stage of 10 leaves, supplied with water of varied quality. We proved the viability of improved drainage and escape water of rice irrigation systems in irrigated agriculture, owing to which maize herbage had been diminished, at an average, by 5.82 %. We verified the negative impact of irrigation water, which contains effluent disposals of metallurgical production, on croppers – it had contributed to diminishing the watered maize herb, at an average, by 39.27 %. Correlation analysis of the test data proved the closely interrelated feedback between the maize herbage amount and the content of cations, anions and water-soluble salts in irrigation water (coefficient of correlation r varied between 0.88 and 0.98). The worked-out linear regressive model for maize herbage, based on the content of water-soluble salts in irrigation water, together with SAR index (Y=2342.71-1.82×x1+366.78×x2), affirmed the validity of the pattern, discovered by means of correlation analysis

Modeling Safflower Seed Productivity in Dependence on Cultivation Technology by the Means of Multiple Linear Regression Model

The results of the study devoted to the evaluation of reliability of the multiple linear regression model for safflower seed yields prediction were presented. Regression model reliability was assessed by the direct comparison of the modeled yields values with the true ones, which were obtained in the field trials with safflower during 2010-2012. The trials were dedicated to study of the effect of various cultivation technology treatments on the safflower seed productivity at the irrigated lands of the South of Ukraine. The agrotechnological factors, which were investigated in the experiments, include: A – soil tillage: A1 – disking at the depth of 14-16 cm; A2 – plowing at the depth of 20-22 cm; B – time of sowing: B1 – 3rd decade of March; B2 – 2nd decade of April; B3 – 3rd decade of April; C – inter-row spacing: C1 – 30 cm; C2- 45 cm; C3 – 60 cm; D – mineral fertilizers dose: D1 – N0P0; D2 – N30P30; D3 – N60P60; D4 – N90P90. Regression analysis allowed us to create a model of the crop productivity, which looks as follows: Y = –1.3639 + 0.0213Х1 + 0.0017Х2 – 0.0121Х3 + 0.0045Х4, where: Y is safflower seed yields, t ha-1; Х1 – soil tillage depth, cm; Х2 – sum of the positive temperatures above 10°С; Х3 – inter-row spacing, cm; Х4 – mineral fertilizers dose, kg ha-1. A direct comparison of the modeled safflower seed yield values with the true ones showed a very slight inaccuracy of the developed model. The maximum amplitude of the residuals averaged to 0.27 t ha-1. Therefore, we conclude that multiple linear regression analysis can be successfully used in purposes of agricultural modeling