Sown Area Research Articles

РОЗВИТОК АГРАРНОЇ СФЕРИ: ТРАНСФОРМАЦІЯ ЛОГІСТИЧНИХ ЗВ’ЯЗКІВ ТА ЕКОНОМІЧНА ЕФЕКТИВНІСТЬ ПІДПРИЄМСТВ ЗЕРНОВОЇ ПРОДУКЦІЇ

The article analyzes the strategic role of Ukraine's agricultural sector in the context of modern globalization and intense international competition. It highlights the significance of grain production enterprises within the country's agro-industrial complex, which play a key role in shaping both domestic and international grain markets. The study addresses the issue of economic efficiency of these enterprises. It is established that the development of effective management approaches and optimization mechanisms for agricultural enterprises will contribute to solving the problem of stabilizing operations and enhancing international competitiveness. The factors influencing the economic efficiency of grain production were examined, taking into account the research of leading scholars, which allowed the development of comprehensive recommendations for improving the conditions of agricultural business. Significant changes in the grain market caused by military actions and the political situation, which significantly reduced the sowing areas and disrupted traditional logistics of export through sea ports, are described. Before the aggression, the main share of grain was exported through Black Sea ports, specifically through Mykolayiv and Odesa, accounting for 95% of all grain shipments. Military actions led to the blockade of these routes, forcing a reorientation of grain export to western ports, resulting in significant logistical costs. The necessity of adapting logistical chains and finding alternative export routes to stabilize agricultural exports and minimize the impact on global food security is emphasized. Based on the analysis, ways to optimize the agricultural sector are proposed, including through state support for farmers, subsidized lending, provision of planting materials and fuel, and the need for demining and ensuring safe working conditions. The prospects for further research in the direction of innovative development of the agricultural sector to enhance its productivity and competitiveness are outlined.

Effects of early and late sowing on the profitability of cucumber (Cucumis sativus L.) production

Objective: To analyze how early and late sowing can improve the income of cucumber producers in the state of Michoacán, Mexico. Design/Methodology/Approach: Monthly income, production cost, profit, and benefit-cost ratio were calculated in three sowing scenarios: middle, early, and late. Results: In an early sowing scenario, the income and profit of the producers increase by 10.2 and 9.2 million pesos, respectively, compared to the middle sowing scenario. Likewise, in a late sowing scenario, producer income and profits increase by 29.2 and 20.0 million pesos, regarding the middle sowing scenario. Study Limitations/Implications: The negative effects on vegetable prices of temporary excess supply prove the need to plan the surface that will be used for early and late sowing, as well as to create a regulatory body that plans the cucumber sowing area. Findings/Conclusions: Sowing cucumber and other vegetables in early or late dates is recommended, since better production planning will increase the producers’ income and profit.

Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions

AbstractPrecise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners.

НАПРАВЛЕНИЯ РАЗВИТИЯ РЫНОЧНО-ОРИЕНТИРОВАННЫХ ОТРАСЛЕЙ РАСТЕНИЕВОДСТВА В ПЕРМСКОМ КРАЕ

The scientific article contains a theoretical analysis of the prospects for increasing production volumes of market-oriented types of crop products, draws attention to the need for the development of organic agriculture, presents the results of an analysis of the global, domestic and regional market for crop products and indicates the fact of imports into the Russian Federation, noting the need to increase consumption of crop products. Attention is drawn to the location of suburban vegetable growing in urban agglomerations, the need to establish the priority of farmers for the production of market-oriented products of the crop industry without losses in their own feed production for the livestock industry is noted. The development of greenhouse vegetable growing in the suburb of Chusovoy, where the Perm Greenhouse Plant operates, was noted. Statistical data is presented and an increase in grain yields in 2022 is noted. In the structure of the gross grain harvest, the largest share belongs to wheat, while the least amount of winter rye was collected. The dynamics of potato yields are considered, it is indicated that peak values were observed in 2011 and 2021. The trend towards a reduction in sown areas has also affected the cultivation of potatoes; according to statistical data, there is a constant annual decrease in the sown areas of potatoes. The decrease in sown areas had a negative impact on the gross potato harvest. Statistically, data indicate that in 2011 the peak value of the gross harvest was obtained with a clear decrease in the sown area. The growth of the gross harvest in 2011 was influenced by the record potato yield. In 2017, there was a decrease in the gross potato harvest due to a significant decrease in yields and sown areas.

ТЕНДЕНЦИИ И ПЕРСПЕКТИВЫ РАЗВИТИЯ ОТРАСЛИ РАСТЕНИЕВОДСТВА В ПЕРМСКОМ КРАЕ

The scientific article contains a theoretical review of scientific literature on the problems of development of the crop production industry. Attention is drawn to the state of the market for crop products, the competitiveness of agricultural sectors and the presence of consumer purchasing preferences in relation to organic products. The dynamics of reduction in sown areas have been revealed, and natural and climatic factors affecting the range, yield and production efficiency have been noted. A methodology for a comprehensive analysis of the conditions for conducting economic activity in the agricultural sector of the economy is presented. The activities of agricultural producers aimed at providing priority provision of their own feed to the livestock industry by reducing the marketable portion of crop production were noted. Solutions for efficient feed production are presented through the selection of promising varieties of feed crops, planning the yield and nutritional value of feed. The strategic directions for the development of the crop production industry have been determined. The priority of development of the crop production industry is confirmed by the presence of deposits and producers of mineral fertilizers. Weaknesses include natural, climatic and soil conditions that limit the list, yield and efficiency of crop production. There are quite a lot of threats to reduce crop production, but the main ones are of a social nature: the continuing outflow of young people from rural areas; Due to labor shortages, there is an increase in abandoned agricultural land. Political, managerial, production and economic opportunities for the development of the crop production industry have been identified in terms of ensuring a targeted approach to personnel training, the development of social and production infrastructure that makes it possible to promote the rural lifestyle among the younger generation and ensure the investment attractiveness of the industry. Thus, great potential for priority development of the crop production industry has been identified.

К ВОПРОСУ ОБ ОБЕСПЕЧЕННОСТИ РОССИЙСКОЙ ФЕДЕРАЦИИ КАРТОФЕЛЕМ СОБСТВЕННОГО ПРОИЗВОДСТВА

The scientific article contains a theoretical review of scientific literature on the problems of increasing potato production in conditions of import substitution, ensuring storage and sales, as well as prospects for reducing imports and developing a potato export network. A study of potato production volumes in 2012 – 2021 was conducted. An increase in potato production volumes was revealed in 2014, 2015 and 2018; in the same years, a decrease in crop production volumes was observed. In 2016, 2020 and 2021, there was a decrease in potato production volumes of up to 12% compared to previous years; in the same years, there was a decrease in its yield (up to 7%) and sown areas (up to 9%). The area under agricultural crops in the Russian Federation occupied more than 117 million hectares, and by 2022 it had decreased to 80 million hectares. The leaders in potato production in 2023 are the Bryansk, Tula, Moscow and Nizhny Novgorod regions; the Perm Territory is not included in the TOP 20 for potato production. Imports of potatoes into the Russian Federation in 2019–2021 did not exceed 5% of the volume of all potatoes produced domestically, which indicates a level of food security in the range of 95–97%. An analysis of potato export volumes in 2019 – 2021 showed a sharp decrease (by half): in 2019, 3% of potatoes produced in the country were exported, in 2020 – 4%, and in 2021 – less than 2%. Thus, there is great potential for priority development of the potato growing industry.

The impact of agricultural practices on groundwater depletion: A spatiotemporal analysis of India

ABSTRACTIn recent decades, intensive agricultural activities have adversely impacted the groundwater resources. The post‐green revolution era in India witnessed excessive irrigation practices that negatively impacted sustainable groundwater utilization. This study aims to analyze trends and patterns of groundwater depletion and identify potential factors to determine groundwater depletion across Indian states for the period 2004–2020. In doing so, percentage of safe blocks in terms of groundwater status is used as proxy for groundwater depletion. Employing the Mann–Kendal Method and Sen's slope, the study examines groundwater trends, while spatial analysis is conducted using Moran's I technique. Furthermore, a Fixed Effect‐Generalized Least Square (FE‐GLS) regression approach is employed to identify factors that influence groundwater status. Findings reveal the spatial autocorrelation of groundwater extraction among Indian states, which reflected by a Moran's I value of 0.62. Additionally, this study found that region of northern and southern India exhibits increasing trend groundwater depletion as compared to other regions. Based on the results of FE‐GLS regression and regression with Driscroll–Kraay standard error, our study indicates that crop intensity, gross irrigated land, gross sown area, and the area sown with water‐intensive crops have a negative and significant impacts on the percentage of safe blocks in terms of groundwater availability. Conversely, we found that annual rainfall has a positive and significant relationship with the percentage of safe blocks. From a policy perspective, the study suggests regulating the cultivation of water‐intensive crops and advocates for integrated policies to ensure sustainable water resource and food security – a balance often termed the food‐water‐energy nexus.

Multi-driving paths for the coupling coordinated development of agricultural carbon emission reduction and sequestration and food security: A configurational analysis based on dynamic fsQCA

Exploring the coupling coordinated development of carbon emission reduction and sequestration (CERS) and food security in agriculture is conducive to mitigating climate change and achieving sustainable development goals. However, there is little research on the coupling coordinated development of these two systems. Based on panel data of 31 Chinese provinces from 2001 to 2021, this study used the dynamic fuzzy set qualitative comparative analysis (fsQCA) method to analyze the levels and driving paths of coupling coordinated development between agricultural CERS and food security. The results revealed that (1) the coupling coordination degree increased from 0.443 in 2001 to 0.489 in 2021, showing an upward trend. (2) We identified three driving paths of coupling coordination development: technology-driven, finance-driven and urban-development-driven. (3) The grain sown area and unit yield are core variables, serving as fundamental pillars, and their combination with technology or financial expenditure enhances this coordinated development. Notably, the grain sown area and unit yield exhibit a complementary relationship, while agricultural technology and financial expenditure demonstrate a substitution relationship. (4) Temporal analysis showed a consistent trend in the technology-driven and finance-driven paths. Regional analysis revealed marked heterogeneity: the technology-finance synergy path predominates in the remote west, the technology-driven path lies in the mountainous southwest, the finance-driven path clusters in the north, and the urban-development-driven path is located in economically advanced regions. Policymakers should formulate policies according to these different driving paths to promote coupling coordinated development.

Environmental risks in agrophytocoenoses under the influence of segetal phytobiota

Abstract In this article the influence of abiotic factors on agrophytocoenoses is considered, these are: moisture supply, rising air temperature, increasing carbon dioxide concentration, and the probability of environmental risks in particular. The degree of ecological risk has been established. Environmental risks due to the influence of climate change on agrophytocoenoses have been identified and substantiated and are: the spread of adventive and invasive plant species; loss of competitiveness of agrophytocoenosis crops relative to segetal phytobiota; increase in the number of species and distribution areas of segetal phytobiota; change in the sown area, productivity and quality of the harvest of agricultural crops; acquisition of tolerance of segetal phytobiota of agrocoenoses to herbicides and deterioration of their effectiveness under agroclimatic fluctuations. All of the above-mentioned environmental risks, which are associated with aggravation of the phytosanitary condition of crops inevitably, lead to a loss of the productivity of agrocoenoses. They lead to a decrease in the quality of the obtained plant and agricultural products as well. Along with the loss of productivity of the weedy agrocoenosis, there are environmental risks caused by the increased need for the use of plant protection products to ensure the control of the phytosanitary state of the agrocoenosis. Ensuring the reduction of environmental risks and enhancing the ecological safety of agrocoenoses is timely environmental monitoring, considering appropriate agronomic, protective, and preventive measures. These measures increase the adaptive capacity of agrocoenoses. r atmospheric levels in analyzing pertinent cases.

Characteristic odor of foxtail millet from different area with different sowing time based on gas chromatography-mass spectrometry

The odor of the foxtail millet is one of the most important indexes to evaluate the foxtail millet quality. Different sowing time results in different quality of the foxtail millet, and the volatiles can be used as fingerprint for distinguish them. The odor components of foxtail millets from spring sowing area and summer sowing area were investigated by means of gas chromatography-mass spectrometry (GC-MS) combined with simultaneous distillation extraction (SDE). A total of 53 volatile compounds were identified in all the samples containing 19 aldehydes, 4 alcohols, 7 ketones, 7 benzene derivatives, 12 hydrocarbons, 3 heterocycles and 1 acid. Aldehydes were the predominant odor components in foxtail millet. Compared with summer sowing area, there were abundant variety and high content of odor components in spring sowing area. Twenty-one volatiles were found to be odor-active compounds. The components with the highest OAVs in most sowing area were (E,E)-2,4-decadienal, (E)-2-nonenal, (E,E)-2,4-nonadienal and nonanal. Finally, 20 volatiles were screened out to distinguish the foxtail millet from spring-sowing area and summer-sowing area by multivariate statistical analysis. The results will be helpful to identify the foxtail millet from different sowing seasons and provide evidence that the spring-sowing foxtail millet shows more active odors.

Impacts of Farrowing Pen Design, Season, and Sow Parity on Litter Performance and Piglet Mortality.

Piglet mortality during lactation is a significant concern in swine production, influenced by complex interactions involving sow, piglet, environmental, and management factors. While crushing by the sow may be the ultimate cause of piglet mortality, there are many factors influencing the outcome, including parity, thermal stress, and animal housing systems. New farrowing systems are continuously being developed; however, it is difficult for producers to make decisions without any scientific basis. This study aimed to assess the impact of different farrowing pen layouts on piglet performance, considering parity and season. A total of 546 sows and 9123 piglets were monitored across 36 lactation cycles. Sows were randomly assigned to three farrowing pen layouts (standard, diagonal, and offset) in three rooms (20 sows by room). All farrowing pens had the same space allocations (2.7 m by 1.8 m and 2.1 m by 0.6 m for the sow area). The three types of farrowing pens were blocked by position within the room. Piglet performance traits (percent of stillborns, percent of mortality, percent of overlays, and average daily weight gain: ADG) and sows traits (health and parity) were monitored following US Meat Animal Research Center (USMARC) procedures. Results indicated that treatment, parity, and season influenced some piglet performance traits. The offset farrowing pen had a lower percent of stillborns compared to the standard. No significant differences were observed between the diagonal crate and the other treatments. When evaluating high mortality sow (>two piglets), the offset and standard treatments had a lower percent of overlays. Piglets from first-parity sows had lower ADG than those from higher-parity sows. A higher percent of overlays were observed in Autumn and Summer compared to Spring and Winter, and Summer had lower average daily weight gain than other seasons. The results suggest that modifying the layout (offset), with sows placed further away from the heating source, can reduce the percent of overlays in sows with high mortality (>2 piglets). In addition, the influence of season on the piglet production traits demonstrated the importance of proper management of the environment, even in systems with a certain level of climatic control.

Investigation of Irrigation Erosion in Pseudopodzol Yellow Soils of the Lankaran District

Inclination, length, form and exposition of the slopes affect the irrigation erosion process in the Lankaran district. The artificial rain in a different intensity was created in the tea plantations of the experiment farm of the tea and citrus plants in Lankaran branch in order to study a reason of the surface washing creation in irrigation erosion of the tea plantations more accurately. It was known that a tea plantation in which the rows were built along the slope in 1975, the liquid flow was 125.0 m3/ha, while the rain intensity was 1.0 mm/min., the liquid dissolved dissolved substances flow was 240.0 m3/ha, 2.90 t/ha in the tea plantations built in 1990, while an amount of the leached soil was 0.80 t/ha. Firstly, conduction of the surface smoothing work is recommended in order to prevent from irrigation erosion in irrigated areas with a slope more than 0.02°. Generally, the surface smoothing work is fundamental and current, and it is divided into two places. The current smoothing work begins from preparation of soil for sowing every year and this measure doesn’t require more funds. The fundamental smoothing work is performed for fundamental smoothing of the irrigated sowing areas. At this time an inclination of the areas shouldn’t be more than 0.01. It is recommended to conduct the fundamental smoothing work, then current smoothing work in the areas where the inclination is more than 0.01. Fight against irrigation erosion, definition of the water and irrigation norm, water consumption and realization in the irrigated areas are main problems. The irrigation norm should be determined before for each plant watering. Beside the aforesaid, the technical means (pipes that are made from different materials), including different brands (Fregat, Kuban, KSID-50, Volzhanka, DDA-100 M and so on) rainmaker aggregates, various progressive irrigation methods (artificial precipitation, subsoil irrigation, drip watering, aerosol irrigation, etc.) should be used.

Drainage network dynamics in an agricultural headwater sub-basin

Headwaters provide many ecosystems services. Currently, these vulnerable systems are subject to threats related to human activities. This work aims to analyse the spatial pattern changes (expansion/contraction) in the drainage network (DN) of a headwater sub-basin under agriculture between 1966 and 2019 in the Argentine Pampas Region. We study and discuss the hydrometeorological and land use context to understand the spatial and temporal dynamics of the DN, and propose a conceptual model that synthesizes the complex interactions between the factors involved in that dynamics. A broad (1950–2019, at the Del Azul Creek basin) and a short (1996–2019, at the sub-basin of the Videla Creek -SVC-) temporal and spatial scale analysis of data were carried out. We studied rainfall, evapotranspiration, water table depth, streamflow and land use. Temporal and spatial changes in the DN of the SVC were analysed by aerial photos and historical satellite images. Four wet and three dry periods were identified, and close surface-subsurface water interactions typical of plains, were found. The area under agriculture showed a first gradual increase (1975–2012), which turned sharp from 2012 (30,908 ha year−1), with a leading role of soybeans' sown area. The area of the DN increased 1.4699*105 m2 between 1966 and 2010, both under dry conditions, which evidenced its expansion. The study of the flatlands' particular hydrology within the current land use and management trends provided key elements to understand DN area's changes. Complex interactions between processes associated with climatic forcing and the system's sensitivity (its state to receive and process the inputs), are involved in the spatial and temporal dynamics of the DN. Our work improves the understanding of the functioning of these vulnerable systems within agricultural areas, nowadays under productive pressures associated with increasing global food demand, and threats to changes in the hydrological dynamics by global change.

The impact of climate change on China's central region grain production: evidence from spatiotemporal pattern evolution

<abstract> <p>Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security.</p> </abstract>

Development of a methodology for automated processing of spatial data of agricultural land based on machine learning

Agriculture industry in Russia is an important part of the economy. Therefore, this industry must be highly efficient and profitable. The usage of remote sensing data for field monitoring makes it possible to simplify the process of collecting information on sown areas and increase the efficiency of this economic sector. Agricultural crops show up well on satellite images and are relatively easy to decipher both in terms of texture and spectral characteristics. However, even for deciphering well-distinguishable objects, large labor costs and high qualification of the performer are required. Recently, active work has been carried out to automate monitoring performed on satellite images, however, these methods are at the initial stage of development and are mostly created for narrowly focused individual areas of activity. The aim of the study is the development of a methodology for automated processing of spatial data of agricultural designation. The novelty of the research lies in the absence of methods for automated decoding in this area. The article describes a study of the possibilities of automated image processing, the analysis of the necessary spectral characteristics and vegetation indices, and the application of the machine learning algorithm used in the methodology. The development of software and hardware implementation of the methodology in Python programming language for geographic information system QGIS is presented. The software and hardware implementation of this methodology allowed to perform automated processing of images of agricultural designation and to identify the objects displayed on them.

FEATURES OF MAIZE GROWING FOR GRAIN IN THE WESTERN FOREST ZONE OF UKRAINE IN THE CONDITIONS OF CURRENT CLIMATE CHANGES

Modern climatic changes, namely significant warming in the northern Forest-Steppe and Polissia zones of Ukraine, provide opportunities for the cultivation of a number of grain and leguminous crops (maize for grain, soybeans, sunflower, and others), previously uncharacteristic of this region. Among the above-mentioned crops, corn has the greatest grain yield potential, therefore it is one of the most common crops in world agriculture. If 10-15 years ago corn was not grown for grain in the Polissia zone, then in recent years its sown areas have grown significantly, and the yield in some years is not inferior to the regions of the Forest Steppe and Steppe, which are traditional for this crop. The results of experimental studies of the station showed that under favorable conditions on the most fertile slopes of the drained sod-podzolic soils of the Western Polissia zone, with intensive technologies, it is possible to obtain more than 10-12 t/ha of corn grain.
 The analysis of hydrothermal indicators shows that with the current indicators of heat supply of the growing season in the zone of Western Polissia on mineral soils, it is possible to achieve not only early-ripening, but even medium-early hybrids of corn with FAO up to 280-300. Drained peat soils, due to their high nitrogen content and sufficient amount of moisture, have sufficient potential for obtaining a high yield of corn grain. However, its indicators over the years of research vary greatly and depend to a large extent on the agro-meteorological conditions of the growing season. It has been established that the main limiting factors for achieving a high yield of corn grain on peat soils are less favorable microclimatic features (less amount of active heat, shorter growing season and frost-free period, etc.) compared to adjacent sod-podzolic soils located nearby on dry land. It has been experimentally established that under conditions of minimum duration without a frost period, only the most early-ripening hybrids of corn with FAO up to 220-240 will have time to form a full-fledged crop of grain on drained peat soils in the Western Polissia zone. By choosing late-ripening hybrids, there is a risk of a significant shortfall in the harvest due to the premature termination of vegetation caused by early autumn frosts. It was established that the highest yield of corn per grain, both on sod-podzolic and peat soils, was provided by the organo-mineral fertilization system, which was based on the application of complete mineral fertilizer at the rate of N90Р90К90 on turf-podzolic soils and N35Р60K120 on peat soils in combination with phosphorus mobilizing drug Rice Pi. The use of the organo-mineral fertilization system (N90Р90К90 + phosphorus mobilizing drug Rice Pi) ensured an increase in the yield of corn on sod-podzolic soils by 30,2 ct/ha compared to the basic fertilization system (N30Р30К30). The use of the organo-mineral fertilization system (N35Р60K120 + phosphorus mobilizing drug Rice Pi) ensured an increase in the yield of corn on peatlands by 56.8 ct/ha compared to the natural background of fertility.

Modeling of production processes in grain sector of the Republic of Kazakhstan: new solutions

The Republic of Kazakhstan is considered one of the countries with the highest potential for grain production in the world, since it has the appropriate infrastructure, the necessary sown areas, harvesting technologies, and human capital. At the same time, high competition and the tasks of ensuring food security of the state require a constant search for ways to improve the effectiveness and rationality of management decisions in crop production. The goal is to determine directions and trends for increasing the effective functioning of grain farming in Kazakhstan. During the research, methods of analysis and synthesis, mathematical modeling and comparison were used. Results - analysis of the volumes of wheat grown in 2010-2022. showed a positive vector of development, the average harvest increases annually by 228 thousand tons, in 2022 22 million tons were harvested in the Republic of Kazakhstan, this is the highest figure in the last 10 years. Of course, this is a satisfactory dynamic, but there are untapped reserves in the industry. Based on the production indicators of the regions of the republic cultivating this crop, the most effective of them were identified. For these purposes, the DEA method was used. Conclusions - the main target function of grain sector is to maximize the satisfaction of the population's needs for grain products. Other, no less important, its functions: creating a balanced market for grains and raw materials for the processing industry; more complete use of production capabilities in all areas of the grain product vertical of the AIC; reduction of losses at the junctions of production - transportation - processing - grain trade. Structural proportions in the development of the grain product subcomplex become a significant factor ensuring its sustainable functioning.

Grain yield estimation of wheat-maize rotation cultivated land based on Sentinel-2 multi-spectral image: A case study in Caoxian County, Shandong, China

Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm (R2=0.843, root mean square error=2.822 kg·hm-2), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation.

Carbon footprint of major grain crops in the middle and lower reaches of the Yangtze River during 2011-2020

The middle and lower reaches of the Yangtze River is one of main grain production areas in China, which is of great significance to food security. Understanding the carbon footprint of major grain crop production is helpful to develop high-yield and low-carbon agriculture. Based on the data of yield, sown area and farmland production input of main grain crops (rice, wheat and maize) in six provinces (Jiangsu, Anhui, Jiangxi, Hubei, Hunan, and Zhejiang) in the middle and lower reaches of the Yangtze River from 2011 to 2020, we estimated carbon footprint in the production of the three grain crops. The results showed that from 2011 to 2020, yield per unit area, planting area, and total yield of rice, wheat and maize were the highest in Jiangsu Province. In terms of area-scaled carbon footprint, rice in the middle and lower reaches of the Yangtze River had the highest area-scaled carbon footprint, with an average of 2.0 t CE·hm-2, followed by wheat and maize. The area-scaled carbon footprint of the three staple crops was increasing. In terms of yield-scaled carbon footprint, rice was the highest, with an average of 0.8 kg CE·kg-1, followed by wheat and maize. In terms of carbon input structure, irrigation electricity, chemical fertilizers and pesticides accounted for a relatively high proportion. Irrigation electricity accounted for 35.0%, 36.3%, and 33.2% of the total carbon input of rice, wheat and maize, respectively. Chemical fertilizers accounted for 28.8%, 32.5%, and 32.5%, respectively, while pesticides accounted for 24.2%, 13.3% and 11.5%, respectively. In terms of carbon efficiency, maize had the highest (3.9 kg·kg-1 CE), followed by rice and wheat. With the green development of agriculture, carbon emission in the production of major grain crops in the middle and lower reaches of the Yangtze River could be reduced by improving irrigation efficiency, fertilizer utilization efficiency, pesticide utilization efficiency and mechanized operation efficiency, as well as diversification of straw returning, cultivation of new varieties and policy leverage.

Роль севооборота в экономике производства подсолнечника в Российской Федерации

On the territory of the Russian Federation, the main cultivated oilseed crop is sunflower. The share of acreage under this agriculture has been steadily growing in recent years. In 2022, they increased by 280 thousand hectares and amounted to 10.0 million hectares, while over the period from 2013 to 2022, the acreage occupied by the crop increased by 38%. According to the Ministry of Agriculture of Russia, in 2022 the sunflower harvest increased by 4.5% compared to 2021 and amounted to 16.4 million tons, which in turn allowed to increase the production of sunflower oil by 14%. Sunflower seeds are in stable demand from domestic oil extraction plants, as well as on the international market, which in turn encourages farmers to increase the area of sowing. However, an increase in the share of sunflower in the structure of sown areas above the scientifically justified level is associated with a real risk of reducing crop yields and reducing the productivity of all crop rotations. According to the research result, the highest crop yield was obtained in the regions where the scientifically justified proportion of sunflower in the structure of crops is maintained.