Aerial application results in production gains in relation to ground application in soybean

Hamada, Beatriz H.; Sganzerla, Marcio L.; Wachtel, Sarah; Lujan, Dionathan W.; Brito Filho, Armando L. de; Silva, Lincon O. S. da; Debona, Daniel; Carneiro, Franciele M.

doi:10.1590/1983-21252025v3812677rc

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Scientific Article • Rev. Caatinga 38 • 2025 • https://doi.org/10.1590/1983-21252025v3812677rc linkcopy

Aerial application results in production gains in relation to ground application in soybean

Aplicação aérea resulta em ganho produtivo em relação a aplicação terrestre na soja

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Grain production plays a significant role in the economy and development of Brazil’s agricultural sector, with a particular emphasis on soybeans. To achieve greater productivity and reduce production costs, the agricultural sector has adopted new technological alternatives, including Remotely Piloted Aircraft (RPA), also known as drones. The use of these aircraft for spraying is intended to provide practical and sustainable control of pathogens and weeds in crops. This study aims to compare terrestrial and aerial application methods to determine which operations provide productive gains in soybean cultivation. To achieve this goal, we monitored operations using Statistical Quality Control (SQC) tools and proximal remote sensing techniques. The experiment was conducted in the experimental areas of the Federal Technological University of Paraná (UTFPR), Campus Santa Helena, Paraná State. The experimental design was based on the basic premise of SQC, comprising 48 sample points for evaluating productivity and 16 sample points for other indicators in each application, totaling 96 points for productivity and 32 points for other indicators. The quality indicators included crop biophysical characteristics and vegetation indices. The statistical analyses included descriptive analyses and SQC tools. It was concluded that RPAs provided a more uniform application, and a significant increase in productivity was observed with aerial application compared to ground application.

Keywords:
Precision agriculture; Statistical control of quality; Glycine max (L) Merril; Application technology.

Operating parameters	Application method
Operating parameters	Terrestrial - Tractorized sprayer	Aerial - RPA T10 model	Aerial - RPA T30 model
Spray Tank (L)	600.00	10.00	30.00
Volume of syrup (L ha^-1)	120.00	13.50	13.00
Speed (km h^-1)	8.00	22.00	20.00
Application height (m)	0.50	2.50	3.00

Date	Commercial product name	Active ingredient	Dosage (0.48 ha)
November 28,2022	Cletodim/Select	Cletodim	0.48 L
December 12, 2022	Zapp; Mancozeb; Elatus; Bold.	Glifosato; mancozebe; azoxistrobina, benzovindiflupir; acetamiprido and fenpropatrina.	0.72 L; 0.72 kg; 0.072kg;
January 1, 2023	Mancozeb; Elatus	Mancozebe, azoxistrobina and benzovindiflupir	0.72 kg; 0.072 kg;

Seasons evaluated*	Aver.	SE Aver.	SD	CV	Min	Med	Max	RJ	P-value
Plant height (m)
Date 1	0.44	0.01	0.03	7.54	0.40	0.43	0.51	0.97^N	>0.10
Date 2	0.65	0.02	0.09	13.45	0.36	0.67	0.76	0.83^NN	<0.01
Date 3	0.76	0.01	0.06	7.28	0.63	0.77	0.83	0.98ⁿ	>0.10
Date 4	0.76	0.02	0.06	7.86	0.62	0.76	0.84	0.97ⁿ	>0.10
Plant canopy length (m)
Date 1	0.37	0.06	0.25	67.83	0.27	0.32	1.32	0.60^NN	<0.01
Date 2	0.36	0.01	0.04	9.90	0.29	0.36	0.43	0.99ⁿ	>0.10
Date 3	0.57	0.15	0.59	103.38	0.32	0.43	2.77	0.56™	<0.01
Date 4	0.32	0.01	0.04	12.80	0.24	0.32	0.39	1.00^N	>0.10
NDVI
Date 1	0.64	0.01	0.05	8.18	0.56	0.63	0.75	0.98^N	>0.10
Date 2	0.64	0.01	0.04	6.61	0.56	0.64	0.70	0.98ⁿ	>0.10
Date 3	0.71	0.02	0.07	9.28	0.55	0.72	0.78	0.94^NN	0.05
Date 4	0.62	0.01	0.03	5.52	0.58	0.61	0.68	0.96ⁿ	>0.10
NDRE
Date 1	0.22	0.01	0.03	11.66	0.19	0.22	0.27	0.97ⁿ	>0.10
Date 2	0.23	0.00	0.01	6.25	0.20	0.23	0.26	0.99ⁿ	>0.10
Date 3	0.27	0.01	0.03	10.26	0.20	0.27	0.30	0.97ⁿ	>0.10
Date 4	0.21	0.00	0.02	7.50	0.19	0.21	0.25	0.93^NN	0.03
Yield (kg ha^-1)
Date 5	1332.30		55.60	385.20	28.91	710.10	1204.50	0.95™	<0.01

Seasons evaluated*	Aver.	SE Aver.	SD	CV	Min	Med	Max	RJ	P-value
Plant height (m)
Date 1	0.44	0.01	0.03	5.73	0.38	0.44	0.48	0.97^N	>0.10
Date 2	0.60	0.02	0.07	10.79	0.47	0.61	0.71	0.97ⁿ	>0.10
Date 3	0.73	0.01	0.05	6.56	0.64	0.73	0.82	0.98ⁿ	>0.10
Date 4	0.75	0.01	0.05	6.95	0.66	0.76	0.87	0.97ⁿ	>0.10
Plant canopy length (m)
Date 1	0.46	0.08	0.34	73.63	0.32	0.39	1.73	0.57™	<0.01
Date 2	0.37	0.01	0.05	13.74	0.31	0.37	0.47	0.98ⁿ	>0.10
Date 3	0.59	0.15	0.60	101.79	0.38	0.44	2.85	0.54^NN	<0.01
Date 4	0.36	0.02	0.07	19.14	0.28	0.35	0.58	0.90^NN	<0.01
NDVI
Date 1	0.63	0.01	0.04	6.86	0.57	0.62	0.72	0.98ⁿ	>0.10
Date 2	0.69	0.01	0.04	6.17	0.62	0.67	0.76	0.97ⁿ	>0.10
Date 3	0.74	0.01	0.03	4.65	0.67	0.75	0.78	0.96ⁿ	>0.10
Date 4	0.54	0.01	0.05	9.47	0.43	0.55	0.64	0.98ⁿ	>0.10
NDRE
Date 1	0.23	0.00	0.02	6.71	0.21	0.23	0.26	0.98ⁿ	>0.10
Date 2	0.26	0.00	0.02	6.19	0.23	0.25	0.29	0.98ⁿ	>0.10
Date 3	0.28	0.00	0.02	5.84	0.26	0.27	0.31	0.98ⁿ	>0.10
Date 4	0.19	0.01	0.02	10.61	0.16	0.19	0.23	0.98ⁿ	>0.10
Yield (kg ha^-1)
Date 5	1711.00		50.90	352.50	20.60	967.80	1739.80	0.99^N	>0.10

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[TFN1] ^*
Date 1 (December 9, 2022), Date 2 (January 11, 2023), Date 3 (February 16, 2023), Date 4 (March 1, 2023), and Date 5 (March 17, 2023 - Harvest date).

[2] Aver. - Average, SE Mean - standard error of the mean, SD - standard deviation, CV - coefficient of variation, min - minimum, max - maximum, RJ - Ryan-Joiner normality test, N - Normal distribution of the data according to the RJ test, NN - Non-normal distribution of the data according to the RJ test.