Management Zones Delineation through Clustering Techniques Based on Soils Traits, NDVI Data, and Multiple Year Crop Yields

Abid Ali, Valda Rondelli, Roberta Martelli, Gloria Falsone, Flavio Lupia and Lorenzo Barbanti
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Abid Ali: Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 50, 40127 Bologna, Italy
Valda Rondelli: Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 50, 40127 Bologna, Italy
Roberta Martelli: Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 50, 40127 Bologna, Italy
Gloria Falsone: Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 50, 40127 Bologna, Italy
Flavio Lupia: CREA Research Centre for Agricultural Policies and Bioeconomy, Via Po, 14, 00198 Rome, Italy
Lorenzo Barbanti: Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 50, 40127 Bologna, Italy

Agriculture, 2022, vol. 12, issue 2, 1-20

Abstract: Availability of georeferenced yield data involving different crops over years, and their use in future crop management, are a subject of growing debate. In a 9 hectare field in Northern Italy, seven years of yield data, including wheat (3 years), maize for biomass (2 years), sunflower, and sorghum, and comprising remote (Landsat) normalized difference vegetation index (NDVI) data during central crop stages, and soil analysis (grid sampling), were subjected to geostatistical analysis (semi-variogram fitting), spatial mapping (simple kriging), and Pearson’s correlation of interpolated data at the same resolution (30 m) as actual NDVI values. Management Zone Analyst software indicated two management zones as the optimum zone number in multiple (7 years) standardized yield data. Three soil traits (clay content, total limestone, total nitrogen) and five dates within the NDVI dataset (acquired in different years) were shown to be best correlated with multiple- and single-year yield data, respectively. These eight parameters were normalized and combined into a two-zone multiple soil and NDVI map to be compared with the two-zone multiple yield map. This resulted in 83% pixel agreement in the high and low zone (89 and 10 respective pixels in the soil and NDVI map; 73 and 26 respective pixels in the yield map) between the two maps. The good agreement, which is due to data buffering across different years and crop types, is a good premise for differential management of the soil- and NDVI-based two zones in future cropping seasons.

Keywords: crop yields; fuzzy c-means clustering; management zones; NDVI; precision field cropping; soil traits; spatial variability (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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