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Background With the increasing development of sophisticated precision farming techniques, high-resolution application maps are frequently discussed as a key factor in increasing yield potential. However, yield potential maps based on multiple soil properties measurements are rarely part of current farming practices. Furthermore, small-scale differences in soil properties have not been taken into account. Methods To investigate the impact of soil property changes at high resolution on yield, a field trial has been divided into a sampling grid of 42 plots. The soil properties in each plot were determined at three soil depths. Grain yield and yield formation of winter wheat were analyzed at two sites. Results Multiple regression analyses of soil properties with yield measures showed that the soil contents of organic carbon, silt, and clay in the top and subsoil explained 45–46% of the variability in grain yield. However, an increasing clay content in the topsoil correlated positively with grain yield and tiller density. In contrast, a higher clay content in the subsoil led to a decrease in grain yield. A cluster analysis of soil texture was deployed to evaluate whether the soil´s small-scale differences caused crucial differences in yield formation. Significant differences in soil organic carbon, yield, and yield formation were observed among clusters in each soil depth. Conclusion These results show that small-scale lateral and vertical differences in soil properties can strongly impact crop yields and should be considered to improve site-specific cropping techniques further.
Background With the increasing development of sophisticated precision farming techniques, high-resolution application maps are frequently discussed as a key factor in increasing yield potential. However, yield potential maps based on multiple soil properties measurements are rarely part of current farming practices. Furthermore, small-scale differences in soil properties have not been taken into account. Methods To investigate the impact of soil property changes at high resolution on yield, a field trial has been divided into a sampling grid of 42 plots. The soil properties in each plot were determined at three soil depths. Grain yield and yield formation of winter wheat were analyzed at two sites. Results Multiple regression analyses of soil properties with yield measures showed that the soil contents of organic carbon, silt, and clay in the top and subsoil explained 45–46% of the variability in grain yield. However, an increasing clay content in the topsoil correlated positively with grain yield and tiller density. In contrast, a higher clay content in the subsoil led to a decrease in grain yield. A cluster analysis of soil texture was deployed to evaluate whether the soil´s small-scale differences caused crucial differences in yield formation. Significant differences in soil organic carbon, yield, and yield formation were observed among clusters in each soil depth. Conclusion These results show that small-scale lateral and vertical differences in soil properties can strongly impact crop yields and should be considered to improve site-specific cropping techniques further.
Varying the rate of application of agronomic inputs generates many positive economic and environmental impacts. Increasingly, technologies that enable variable rate application are becoming a distinctive feature of precision agriculture. Nonetheless, a prerequisite, and crucial challenge, remains the optimal and operational designation of distinct application zones for differing agronomic operations. Core to this challenge is the conflation and fusion of diverse data sources ranging from satellite imagery to realtime in-situ data from farms. At present, zones for variable rate application are often defined manually by agronomists and farmers. This paper proposes a novel methodology for the automatic definition of zones for variable rate application. This approach comprises multi-dimensional spatio-temporal data integration methods, clustering-based data classification and a zone creation and representation procedure. In this way, the harmonization of heterogeneous data sources, augmented with different clustering algorithms, enable the delineation of management zones and subsequent construction of maps for potential variable rate applications. Experimental results confirm the effectiveness and efficiency of the proposed approach.
The most important aspect of precision farming is the prediction of crop yield and quality. Digital technologies (soil maps and combine harvester with telemetry functions) were used to determinate the yield of organically grown winter wheat (variety Skagen) in two fields of 18.8 and 4.5 ha in Lithuanian regional conditions, in an area classified as low-performance for farming. The objective of the research was to determine the effectiveness of digital technologies (soil maps and combine harvester with telemetry functions) in assessment of the dynamics of soil pH, P2O5, and K2O, humus and organic winter wheat (variety Skagen) productivity, and grain crude-protein dependence in low-performance soils. Haplic Luvisol soils predominated, while Eutric Gleysols, Haplic Arenosols, and Eutric Planosols soils intervened in smaller areas, and the granulometric composition of the soil in the arable layer and the subsoil varied from sand to sandy loam, loam, and silt loam. In the sandy areas of Haplic Arenosols and in the lower parts of the field, where Eutric Gleysols, intervened in predominant Haplic Luvisols soils, winter wheat crude protein content and grain yield were lower. The biggest grain yield of 6.95 t ha−1 was obtained in Haplic Luvisols soils. Crude protein of winter wheat grains varied from 9.70 to 13.34%. Although both technologies reflected the non-uniform yields of the fields and correlation between them well, the information on the soil cover of the field better explained the reasons for lower yields. In the case of this research, sand inclusions and lower areas in winter wheat fields, causing plants to soak during winter, were identified. The combination of two digital technologies (soil maps and combine harvester with telemetry functions) made it possible to determine yields accurately, and quickly. Moreover, there is a need, in the future, to evaluate the reasons for yield variation and address changes in yields due to the improvement of certain low-performance soil areas. The complex use of these technologies can be beneficial in terms of labour and economy. However, the accurate benefit of labour time and economic should be investigated.
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