THE INFLUENCE OF DIFFERENT CROP ROTATIONS AND FERTILIZATION SYSTEMS ON CHERNOZEM SOIL BULK DENSITY

The soil health can be deduced by chemical, biological and physical properties. This triad of features influence each other and equally determines soil quality and fertility. The paper includes the study regarding physical state of the chernozem soil characterized by bulk density – soil physical property that estimate soil compaction. The study took place in long-term field experiments of the Selectia Research Institute of Field Crop located in the North part of Moldova. The experimental data were obtained in 2019-2020 agriculture year. The soil bulk density, studied in different crop rotations and fertilization systems, was determined under winter wheat agrocenosis after harvesting in the 0-40 cm soil layer. The researches has shown that chernozem soil bulk density registered more favorable indices in crop rotations that include: perennial legumes and grasses in a mixture or only perennial legumes; less row crops - which means minimizing tillage (mechanic disturbance of soil). Regarding fertilization systems – the soil compaction is lower on the plots with adequate amount of organic fertilizer.

Analyzing Effects of Crops on SMAP Satellite-based Soil Moisture using a Rainfall-Runoff Model in the U.S. Corn Belt

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

Analysis of Selected Water Quality Indicators from Runoff during Potato Cultivation after Natural Precipitation

When growing wide-row crops on sloped lands, there is significant surface runoff. In relation to the runoff process, potatoes are classified as a risk crop. This study aimed to grow potatoes in the Bohemian-Moravian Highlands, where the protection zone of the water supply reservoir of Švihov is also located. At selected experimental areas, water samples were taken after precipitation events when surface runoff and water erosion occurred. These samples were analysed (nitrates, total P, and selected pesticides used for potato growing) in an accredited laboratory. We located three different variants of nitrogen fertilisation in each experimental area. Precipitation and the amount of water from surface runoff after each higher precipitation event were also measured in the experimental areas. By knowing the acreage of each experimental area, the volume of surface runoff water and the concentration of nitrates, phosphorus, and pesticides, it was possible to calculate the balance of these substances. We also calculated the percentage of surface runoff. The results imply that a new potato cultivator in the technology of stone windrowing should be designed for weed control as part of a weed control system with reduced herbicide application requirements. Innovative agrotechnical processes reducing pollution of water sources by phosphorus and nitrates should also be enhanced. These are based on a precise application of mineral fertiliser into the root area of plants within the period of an intensive intake of nutrients.

Research of the chisel with multi-operation working bodies for tillage

The mechanized technologies applied in Russia in vineyards are characterized by 2.5-3.4 times higher energy costs comparing to foreign countries. It is necessary to improve the methods of mechanized tillage in vineyards in order to reduce energy costs while maintaining quality. The purpose of the study is to develop mechanized universal technical means and working bodies for processing vineyard rows with the lowest energy consumption without damaging the root system of the bushes. The designed multi-stage chisel is capable of performing multi-tiered tillage in accordance with the architectonics of the root system of the bushes. The depth of loosening is 9-45 cm. The original design of the working bodies of the chisel with the additional degree of freedom provides reduction in traction resistance by 12-14% and specific energy consumption by 1.2 and more times. Chisel complies with agrotechnical requirements for the implementation of quality indicators of the technological process. The number of fractions of up to 50 mm is 57.5-76.5%, lumps of over 100 mm is 8-9%. The versatile design of the multi-operational horticultural and vineyard chisel will allow it to be used in the cultivation technologies of not only fruit and berry, but also grain and row crops.

Estimating Leaf Area Index in Row Crops Using Wheel-Based and Airborne Discrete Return Light Detection and Ranging Data

Leaf area index (LAI) is an important variable for characterizing plant canopy in crop models. It is traditionally defined as the total one-sided leaf area per unit ground area and is estimated by both direct and indirect methods. This paper explores the effectiveness of using light detection and ranging (LiDAR) data to estimate LAI for sorghum and maize with different treatments at multiple times during the growing season from both a wheeled vehicle and Unmanned Aerial Vehicles. Linear and nonlinear regression models are investigated for prediction utilizing statistical and plant structure-based features extracted from the LiDAR point cloud data with ground reference obtained from an in-field plant canopy analyzer (indirect method). Results based on the value of the coefficient of determination (R2) and root mean squared error for predictive models ranged from ∼0.4 in the early season to ∼0.6 for sorghum and ∼0.5 to 0.80 for maize from 40 Days after Sowing to harvest.

Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.

MULTI-PURPOSE CHISEL FOR SOIL TREATMENT IN VINEYARDS

Used in Russia mechanized technology in the vineyards characterized by an increase in en-ergy costs 2.5–3.4 times compared to foreign countries. Most of the energy costs account for processing vineyard soil. It is necessary to improve the methods of mechanized soil cul-tivation in vineyards in terms of reducing en-ergy consumption while maintaining quality. The aim of the study is to develop mecha-nized universal technical means and working bodies for processing row-spacing of vine-yards with the lowest energy consumption without damaging the root system of the bushes. The designed multioperational chisel is capable of performing tiered tillage in ac-cordance with the architectonics of the root system of the bushes. Loosening depth 9–45 cm. The original design of the working bodies of the chisel with an additional degree of freedom provides a reduction in traction resistance by 12–14% and specific energy consumption by 1.2 and more times. Chisel complies with agrotechnical requirements in terms of fulfilling the quality indicators of the technological process. The number of frac-tions up to 50 mm 57.5–76.5 %, lumps over 100 mm 8–9 %. Universal design multiopera-tional garden and vineyard chisel allow its use in technologies of cultivation not only fruit, but also grain and row crops.

The effect of biological products and microelement fertilizer ‘Organomix’ on maize productivity in the southern part of the Rostov region

The current paper has presented the study results on the effect of biological products and microelement fertilizers ‘Organomix’ on productivity of the maize hybrid ‘Zernogradsky 354MV’ carried out in laboratory for cultivation technologies of row crops (FSBSI “ARC “Donskoy”) in 2019–2020. The soil in the experimental plot was favorable for the cultivation of corn, containing 3.36% of humus in the arable layer, 24.4 mg of mobile phosphorus, and 360 mg of exchangeable potassium per 1 kg of soil. The soil pH was 7.0. The study was carried out to estimate the effect of the use of biological products for seed treatment and microelement fertilizers ‘Organomix’ for plant treatment on productivity and economic efficiency of maize cultivation. There was low moisture content of sowings during the period of the trial. There was established an uneven distribution of precipitation, the value of the hydrothermal coefficient was less than 1 (0.64 in 2019 and 0.65 in 2020), which indicated the dryness of the vegetation period. The studied biological products and microelement fertilizers influenced the yield structure elements. The applied biological products and microelement fertilizers ‘Organomix’ improved survival rate of plants before harvesting (the plant density was 4.39–4.54 pcs/m2). There was increase of grain productivity indicators, namely cob weight ranged from 112.9 to 125.7 g, grain weight per ear varied from 94.4 to 104.8 g and 1000-grain weight was 221.2–231.4 g. The improvement of the yield structure elements increased grain productivity on 0.25–0.77 t/ha. Economic efficiency showed that the use of biological products and microelement fertilizers raised the conditional net income to the level of 28 061–34 821 rubles/ha, profitability up to 167.6–201.8% and reduced production costs to 4640–5231 rubles/t.

A CONUS-wide Standardized Precipitation-Evapotranspiration Index for Major US Row Crops

Agricultural droughts afflicting the contiguous United States (CONUS) are serious and costly natural hazards. Widespread damage to a single cash crop may be crippling to rural communities that produce it. While drought is insidious in nature, drought indices derived from meteorological data and drought impact reports both provide essential guidance to decision makers about the location and intensity of developing and ongoing droughts. However, response to dry meteorological conditions is not consistent from one crop type to the next, making crop-specific drought appraisal difficult using weather data alone. Additionally, drought impact reports are often subjective, latent, or both. To rectify this, we developed drought indices using meteorological data, and phenological information for the row crops most commonly grown over CONUS: corn, soybeans, and winter wheat. These are referred to as crop-specific standardized precipitation-evapotranspiration indices (CSPEIs). CSPEIs correlate more closely with end-of-season yields than traditional meteorological indicators for the eastern two thirds of CONUS for corn, and offer an advantage in predicting winter wheat yields for the High Plains. CSPEIs do not always explain a higher fraction of variance than traditional meteorological indicators. In such cases, results provide insight on which meteorological indicators to use to most effectively supplement impacts information.