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The hypothesis of this study is that it is possible to determine the plant stand in the soybean (Glycine max L. Merril) crop based on the spatial variability of management units, which are limiting factors in maximizing crop yield. Our objectives were as follows: (I) to evaluate the relationship between soil physical and chemical attributes to establish potential management units for variable-rate seeding; (II) to propose a method for varying plant stands based on the law of minimum soil nutrients; an (III) to relate the interaction between different plant stands on soybean grain yield, taking into account the interaction between the spatial variability of the mapped attributes. Field experiments were carried out on two plots over two agricultural years. The areas were seeded by randomly varying the soybean stand across strips in the first year. The most limiting soil nutrient was established and used, together with the soil CEC, to determine management units (MUs), which were also used to seed soybeans in VRT (Variable Rate Technology) in the same plots in the second year. MUs with the lowest restriction for maximizing yield were sown in the second year with the lowest plant stand. Data were processed using multivariate statistics. Our findings reveal that it is possible to establish MUs for seeding soybeans with different stands following the spatial variability of limiting soil nutrients according to the law of the minimum and thus increase the crop grain yield. Spatial variability of potassium (K) in the plot, identified as limiting, affected the spatial variability of grain yield. Decreasing plant stands in MUs with the lowest limitation level increases yield. However, increasing the stand in MUs with a higher limitation level can lead to increased intraspecific competition, affecting yield as well as increasing input costs. [ABSTRACT FROM AUTHOR]

The hypothesis of this study is that it is possible to determine the plant stand in the soybean (Glycine max L. Merril) crop based on the spatial variability of management units, which are limiting factors in maximizing crop yield. Our objectives were as follows: (I) to evaluate the relationship between soil physical and chemical attributes to establish potential management units for variable-rate seeding; (II) to propose a method for varying plant stands based on the law of minimum soil nutrients; an (III) to relate the interaction between different plant stands on soybean grain yield, taking into account the interaction between the spatial variability of the mapped attributes. Field experiments were carried out on two plots over two agricultural years. The areas were seeded by randomly varying the soybean stand across strips in the first year. The most limiting soil nutrient was established and used, together with the soil CEC, to determine management units (MUs), which were also used to seed soybeans in VRT (Variable Rate Technology) in the same plots in the second year. MUs with the lowest restriction for maximizing yield were sown in the second year with the lowest plant stand. Data were processed using multivariate statistics. Our findings reveal that it is possible to establish MUs for seeding soybeans with different stands following the spatial variability of limiting soil nutrients according to the law of the minimum and thus increase the crop grain yield. Spatial variability of potassium (K) in the plot, identified as limiting, affected the spatial variability of grain yield. Decreasing plant stands in MUs with the lowest limitation level increases yield. However, increasing the stand in MUs with a higher limitation level can lead to increased intraspecific competition, affecting yield as well as increasing input costs.

Precision seeding which exploits the variability of soil properties in the field, is one of the most important agrotechnological solutions for smart agriculture, making it possible to increase the agronomic and economic efficiency of the production of one of the world's most popular crops—winter wheat. The aim of this work was to investigate the impact of the site-specific-seeding (SSS) method on winter wheat yield and its productivity parameters and economic benefits compared with the conventional uniform rate seeding (URS) method. The experimental studies were carried out in a 22.4 ha field, which was divided into 5 soil management zones (MZs) based on the measured apparent electrical conductivity (ECa) with an electromagnetic induction sensor. These included MZ1 representing the highest soil ECa zone, MZ2, MZ3, and MZ4 as the medium-high, medium, and medium-low zones, respectively, and finally MZ5 as the lowest ECa zone with the lightest soil texture. The studies were carried out using two seeding methods. Under the conventional URS method, the same seeding rate of 180 kg ha−1 was applied in all MZs, while under the precision SSS method different seeding rates ranging from 146 kg ha−1 (MZ1) to 214 kg ha−1 (MZ5) were applied. Results showed that the SSS method overcome the URS in providing higher average grain yield and its yield components (e.g., the number of ears per square meter, the number of grains per ear, and the weight of 1000 grains). A particularly strong effect of seeding methods was found in the poorest soil fertility zone MZ5, where a significant difference between SSS and URS was obtained concerning plant height, straw-to-grain ratio, number of grains per ear, weight of 1000 grains, and grain yield. The cost-benefit analysis showed that the SSS approach resulted in an 8.3% higher gross margin than the URS approach. Future research is necessary to validate the results obtained in a larger number of fields having different degrees of spatial variability. [ABSTRACT FROM AUTHOR]

Sustainable and responsible agricultural production is one of the keys to keeping people, animals, soil, and the environment healthy. Precision seeding technologies for winter wheat, exploiting the variability of soil properties and adapting the technological processes of variable rate seeding and variable seeding depths, are essential not only to improving plant productivity and economic benefits but also to cleaner agricultural production. This work aimed to carry out a life cycle assessment (LCA) of winter wheat production and determine the environmental impact of different precision seeding technologies in terms of individual impact categories compared to conventional seeding technology. Experimental studies were carried out between 2020 and 2022 using conventional uniform seeding rate (URS) and several precision seeding technologies: in the first year—VRS for variable seeding rate and VRS + VRF for variable seeding rate and fertilizer rate, and in the second year—VRS and VRSD for variable seeding rate and variable depth, and VRSD + VRF for variable seeding rate, variable depth, and variable fertilizer rate. The results obtained for winter wheat grain yield showed that the effect of precision seeding technology on the increase of grain yield was not significant compared to the URS. A greater influence on grain yield was found in individual soil management zones, especially in the zone with the worst soil fertility. The LCA did not show any significant differences between precision seeding technology and conventional technology in any of the environmental impact categories. The GWP values (0.200–0.236 kg CO2eq kg−1) were most dependent on grain yield, as precision seeding technology had small changes in the amount of inputs (seeds and fertilizers), while all other technological operations were the same as under the URS technology. The amounts of phosphorus and potassium fertilizers decreased by 1.4 and 7.9%, respectively, and the amounts of winter wheat seeds and nitrogen fertilizers increased by 4.1 and 5.4%, respectively, compared to the URS. [ABSTRACT FROM AUTHOR]

Most of the motor drivers and monitoring sensors used in domestic electric drive precision maize planter are directly connected to the main controller. It’s difficult to expand the number of sowing rows and cannot meet the requirements of variable sowing operation for independent control of each single sowing unit. In order to reduce the coupling of the control system and alleviate the calculation pressure of the main controller, a hardware structure of row-unit controller based on STM32F103 was built, including motor drive circuit and seed quality detection circuit. The P-N 3-phase full-bridge BLDC (brushless direct current) motor drive circuit was based on FAN3278, which could regulate the rotate speed and prevent overload. The seed quality detection circuit was based on photoelectric sensor, which could realize photoelectric isolation and shaping of the sensor signal. The results of signal integrity test indicated that ripple voltage reduced 46.5%, signal transmission of each module was stable and working logic was reasonable. Moreover, a software architecture of the row-unit controller was constructed, the core algorithm included BLDC motor driving program and seed quality detection program was designed. A PID closed-loop control algorithm was used for BLDC motor driver to overcome the shortcomings of open-loop control. The final tuned PID parameter values：Proportional constant was 0.000 04，integration constant was 0.000 3，differential constant was 0.000 01. The results of no-load starting test on BLDC motor indicated that: within a set speed range of 300 -1 800 r/min, the max rise time was 0.4 s and the maximum overshoot was 1.56% on the speed response curve, the start performance met the requirement and speed of motor was improved. The data exchanged between the row-unit driver and the main controller via the CAN bus. The overall test results of the row-unit driver confirmed that it could realize the function of the seed metering driving, the seeding quality detection, and CAN communication with the main controller. The performance test results of seed metering device driving showed that the qualified index was more than 95.7% , the miss index was less than 4.3%, and the multiple index was less than 1.4% when travel speed was 3-9 km/h, which was higher than the national standard GB/T 6973-2005 requirement. The comparison test between the designed single unit driver and seed meter detector for performance evaluation revealed that the detection results were basically coincident. The maximum difference of precision index was only 0.1 percentage points when the speed was 3 km/h. The difference of the qualified index between the seed meter detector and the single unit driver was no more than 2 percentage points, the multiple index was no more than 1.1 percentage points, and the miss index was no more than 0.9 percentage points under different speed gradients. To sum up, the performance of the developed row-unit driver is applicable, and it can be applied in variable rate seeding. [ABSTRACT FROM AUTHOR]