Differentiators of precision agriculture on small properties

Evaluation shows that adopting precision agriculture increases productivity when compared to conventional agriculture on small properties

16.06.2020 | 20:59 (UTC -3)

Evaluation shows that adopting precision agriculture increases productivity when compared to conventional agriculture on small properties.

The search for high crop productivity is one of the principles sought by the producer, for this reason, knowing the variability of soil attributes has facilitated the management of the area to define better decision-making. Therefore, precision agriculture is a set of techniques that allows localized crop management (Balastreire, 1998). However, the major obstacle to adopting precision agriculture techniques is the high cost of purchasing equipment and implementing the system, which does not always guarantee its return and economic viability, especially for small rural producers and family farming.

The concern regarding the adoption and application of these technologies refers to the great availability of information, requiring technicians and agronomists with knowledge and experience in interpreting the collected data, in the form of maps (Mantovani et al.

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Knowing these limitations, the objective of this work was to evaluate the productivity and variability of the chemical attributes of the soil and the productivity of corn crops in relation to the use of tools available for precision agriculture compared to conventional agriculture in a productive area of Instituto Federal Catarinense (IFC) – Campus Rio do Sul. The area was conducted in a production unit on the IFC experimental farm - Campus Rio do Sul, in Santa Catarina. The experiment consisted of ten plots of 256m², which were divided into five precision agriculture (AP) plots and five conventional agriculture (AC) plots.

The plots were georeferenced using a Topcon Hiper II RTK GPS, with UTM coordinates. Soil samples were collected by a drill type auger, at a depth of 0,2m randomly in the plots (AC), forming a single composite sample, while in the plots (AP) they were collected in a localized manner, removing nine subsamples per plot, forming a composite sample per plot and sent for laboratory analysis. The results of the soil analyzes were interpreted, and maps of spatial variability of recommended fertility and final productivity were created using the GS+ Geostatistics software program.

The experimental area of the Instituto Federal Catarinense resembled the production system of a small rural property.

Sketch of the experimental area where field analysis was carried out.

Fertilization was carried out according to the interpretation of the soil analysis and recommendation for corn cultivation in a localized manner for the plots (AP) and homogeneously for the plots (AC). Corn sowing was defined with a population of six plants/m² and spaced 0,80m between rows and adjustment was carried out manually both for population density and for fertilization, which was carried out in accordance with the recommendation of the Fertilization and Liming for the states of RS/SC in their respective installments.

During the development of the crop, spraying was carried out in accordance with agronomic management recommendations and top dressing was applied as recommended by the interpretation of the soil analysis. Harvesting was carried out manually, in each plot containing three 10m repetitions.² to obtain the final average productivity. After threshing and drying the grains, the final productivity of the crop was calculated, transferring the data to the productivity map.

The spatial variability of the chemical attributes of the soil was evident due to statistical analysis, according to Nogueira (2007), a coefficient of variation greater than 35% reveals that the series is heterogeneous, that is, the soil must be managed in a localized manner, according to the results of the soil analysis for each management zone (Table 1).

With the results obtained from soil analyzes and their variations, a map of the interpretation of the need for nitrogen, phosphorus and potassium was created, as shown in Figure 1.

Figure 1 - Map of the need for NPK in the soil

Therefore, the variability of the amount of fertilizer to be used was verified, resulting in the following fertilization adjustments, according to the interpretation of soil analyzes and recommendations for corn cultivation, and in the AP1 and AP5 plots in red color, an amount of 400kg/ha of the formulation (7-28-14), while in the AC, AP2, AP3 and AP4 plots, in green, 450kg/ha of the same formula were added. The top dressing used was urea (45% nitrogen), in the following doses, 220kg/ha (AP3), 230kg/ha (AP1), 264kg/ha (AP2, AP4 and all ACs) and 275kg/ha (AP5 ).

Therefore, in matters of comparing averages between the AP and AC plots, there were variations in the application of fertilizer located in the plots evaluated. In Figure 2, the corn crop productivity map can be seen, which varied due to localized fertilization depending on the spatial variability of the soil's chemical attributes, that is, the management of these areas was treated individually, even using two application doses. at a variable rate, according to the needs of the plot and the crop, thus significantly expressing the increase in final productivity of AP plots compared to AC plots.

Figure 2 - Maize productivity map in the 2014/2015 harvest

The AP plots obtained the highest productivity values, with an average value of 10.014,2kg/ha (166,9sc/ha), with the average AC productivity being 8.446,1kg/ha (140,8sc/ha), as per Table 2. An average increase of 18,5% in productivity was observed, a result explained by the effect of localized fertilization in accordance with the requirements. In this way, it was found that precision agriculture can be adopted on small and family properties with existing technologies on the property.