Chickpea (Cicer arietinum L.) is one of the main pulse crops cultivated mostly in the arid and semi-arid regions of the world, very often on saline lands. The problem is that it has not been clearly determined yet what is the safe salinity degree for obtaining uniform and vigorous sprouts of the crop without significant suppression in the parameters of initial growth and development. The goal of our study was to determine the effect of different NaCl concentrations in solutions on chickpea germination and initial growth to determine the safe degree of salinity for the crop cultivation. The study was carried out in greenhouse conditions of Kherson State Agrarian University. We studied the effect of five different gradually increasing degrees of NaCl solutions on the germination percentage and initial growth of chickpea (variety Rosanna, kabuli type) that was germinated in laboratory conditions in flasks filled with sand, at the temperature of 25 oC. A significant decrease in all the studied parameters was observed with the increase of salinity degree. However, we think that a considerable decrease of the crop germination and initial growth started with NaCl concentration of 1.79 g/L: germination percentage decreased by 33.9%, plant height – by 7.8 cm, root length – by 5.5 cm in comparison to the control variant (not saline conditions). Therefore, we conclude that the chickpea can be efficiently cultivated on slightly-saline lands. Besides, the results of linear regression analysis revealed that the most susceptible stage of chickpea growth and development is germination because this stage had strong close inter-connection with the degree of salinity. Further growth of the crop was less affected by the salinity stress. We recommend cultivation of chickpea on the saline lands only with a slight salinity level.

Chickpea is supposed to be a prospective crop for soil reclamation. The goal of this study was to determine the effect of tillage and humidification conditions on the chickpea desalination properties. The study was conducted by using the randomized split plot method in four replications during 2012-14 at the Agricultural Cooperative Farm «Radianska Zemlia» of Kherson region in Ukraine. The results of the study showed that the maximum salts uptake of 2.516 t ha -1 from the 0-50 cm soil layer and the maximum chickpea grain yield of 3.33 t ha -1 were provided under irrigated conditions with moldboard plowing on the depth of 28-30 cm. It was established that the higher chickpea grain yield is, the greater the salts uptake rate from the soil. It was also proven that the plowing depth has no significant effect on the chickpea grain yield and desalination properties. It should be mentioned that chickpea showed limited desalination properties. The crop was not able to adsorb all the sodium from the soil when irrigated with saline water.

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Crop models are of great use and importance in modern agriculture. Most models imply spatial vegetation indices, such as NDVI, or canopy cover characteristics, such as FGCC, to provide estimation of crops conditions and forecast productivity. The purpose of the study was to (1) determine the possibility of mutual conversion between spatial NDVI and Canopeo-derived FGCC in five crops (grain corn, sunflower, tomato, millet, and winter wheat) and (2) estimate the precision of such a conversion. The data set of the study was formed by the OneSoil AI derived satellite imagery on NDVI for the studied crops in different stages of their growing season combined with Canopeo-processed photographs of vegetating crops in the field with FGCC percentage calculation. The sets of NDVI and FGCC values were paired up and then statistically processed to obtain polynomial equations of NDVI into FGCC and inverse conversion for each crop. The results of the study revealed that mutual conversion between spatial NDVI and Canopeo-derived FGCC is possible. There is a strong direct correlation (R2 within 0.6779–0.9000 depending on the crop) between the studied indices for all crops. Close-growing crops, especially winter wheat, showed the highest correlation, while row crops and especially tomatoes had a less strong relationship between vegetation indices. The models for mutual conversion between FGCC and NDVI could be incorporated into the yield simulation models to improve the forecasting capacities.

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