Effect of Precision Seeding Technology on Corm Yield Based on GIS Data - Case Study

Horváthné Kovács, Bernadett – Barna, Róbert – Csonka, Arnold – Tóth, Katalin – Hoffmann, Richárd

Keywords: data-driven decision making, precision farming, linear modelling, maize yield, seeding technology, C12, C51, Q12, Q16

The authors analysed precision data of an agricultural farm situated in Somogy county in South-Transdanubia of Hungary. The objective of the analysis was to help the farmer's decision about variable seed rate application according to the management preferences. FAO 470 maize harvest yields, the prescribed seeding rate, the actual seeding amount per hectare of seeding machines and harvesting data were used. Additionally, the topography's GIS data were collected. This paper reports the results of the analysis of differentiated seeding efficiency and effect on the harvest yields. A spatial grid of the corresponding precision data was created with QGIS. The total number of observations was 6110, each was assigned the extrapolated data. Linear models were used to explain the effect of categories of the prescribed seeding rate and type of seeding machines. Bonferroni method and Student's test were used to test the pairwise comparisons. Our results proved that differentiated seeding has effect on the harvest yield, but both the type of the seeding machine and the variance in the soil yield capacity modify this power. If the seeding rate is increased by 10 thousand per hectare, the yield of maize is increased by 0.1 ton in average. The type of the seeding machine may affect the results by 0.77 ton. The results reported in this paper contribute to the maize yield planning and management practice of agricultural companies. In a later analysis the authors aim to develop a multivariable yield prediction model by involving the slope gradient and aspect of the soil and yield capacity patterns of the crop field. The analysed company continues to register the precision data of the cultivation of sample field, thus research on maize yield mapping will be also possible in the future. The publication of this paper is supported by the EFOP-3.6.2-16-2017-00018 “Produce together with the nature - agroforestry as a new outbreaking possibility” project.

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