Uzbekkhon Mukhtorov

This article examines the effective use of irrigated lands based on cadastral valuation using the examples of Uzbekistan and Kazakhstan. The study includes a comparative analysis of cadastral indicators of irrigated lands, soil quality, land reclamation status, and productivity. The results confirm the feasibility of using cadastral valuation data in land use planning and improving the economic efficiency of agriculture.

This article examines the importance of cadastral valuation of agricultural land for improving land use eficiency. It analyzes the impact of cadastral valuation results, based on natural and economic indicators of land, on production eficiency. It also presents scientific findings and practical approaches to improving the cadastral valuation system.

Land used for agriculture is critical in Uzbekistan and throughout the world. Land reclamation is assessed using a variety of traditional approaches. The creation of land reclamation assessment methods is becoming more common as a result of the usage of modern geoinformation technologies and their better methodology. The levels of mineralization in the groundwater level, as well as NDVI and NDSI analysis, were determined for the three months of 2020 and 2021 - April, July, and November - in the irrigated lands of the Yuqiri chirchik, Urtachirchik, and Quyichirchik districts of the Tashkent region, which were used as the research object. These data were reclassified in ArcGIS software, and fields were defined by values. Their correlation was determined based on the results provided above. It was determined that plant vegetation is dependent on the salinity and mineralization of seepage waters, i.e., a decrease in seepage water mineralization and a drop in salinity are directly associated to an increase in plant vegetation, and vice versa.

Global population growth and the deepening of globalization processes make it necessary to increase the production of food and other material goods for human consumption in proportion and to improve their quality from an environmental point of view. The analysis shows that in world practice, this problem is often solved by extensive factors, mainly by expanding the area under agricultural crops. However, the limited amount of available arable land is estimated at Rs 1,500 mln. hectares. Therefore, it can be said that almost all the reserves of arable land have been used by mankind. Therefore, the development of additional lands and the prevention of the possible ecological crisis, and on this basis the efficient use of arable land, in turn, necessitates the reproduction of natural resources on the basis of innovative solutions and ensuring a balance between human economic activity. The role of land melioration as one of the factors of intensification of agricultural production is high and plays a primary and key role in ensuring the efficiency of the sector.

Today in Uzbekistan, fines are imposed for sanctioning violations of agricultural lands, which are used irrationally in protecting agricultural lands and preventing them from escaping from agricultural turnover. The calculation of these fines is included in the category of administrative offenses and is punishable by fines. This is a very recurring violation of the use of agricultural land for purposes other than those intended for illegal use. The imposition of fines is not sufficient to determine the amount of the basic calculation in Uzbekistan, and it is not effective enough to protect them. In order to increase the effectiveness of sanctions, it is advisable to impose fines for the illegal use of agricultural land in the protection of agricultural lands, taking into account the size of the land, the soil fertility, and the yield that can be obtained from it. Because it significantly increases the amount of fines and allows you to direct these fines for the efficient use of these lands. Therefore, this article offers suggestions for improving the method of calculating the amount of fines for improper use of agricultural land, taking into account the size of the land, soil fertility, and productivity.

Timely crop yield information is needed for agricultural land management and food security. We investigated using remote sensing data from the Earth observation mission Sentinel-2 to monitor the crop phenology and predict the crop yield of sunflowers at the field scale. Ten sunflower fields in Mezőhegyes, southeastern Hungary, were monitored in 2021, and the crop yield was measured by a combine harvester. Images from Sentinel-2 were collected throughout the monitoring period, and vegetation indices (VIs) were extracted to monitor the crop growth. Multiple linear regression and two different machine learning approaches were applied to predicting the crop yield, and the best-performing one was selected for further analysis. The results were as follows. The VIs showed the highest correlation with the crop yield (R > 0.6) during the inflorescence emergence stage. The most suitable time for predicting the crop yield was 86–116 days after sowing. Random forest regression (RFR) was the best machine learning approach for predicting field-scale variability of the crop yield (R² ∼ 0.6 and RMSE 0.284–0.473 t/ha). Our results can be used to develop a timely and robust prediction method for sunflower crop yields at the field scale to support decision-making by policymakers regarding food security.