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<p>Durum wheat (<i>Triticum durum</i> Desf.) yield should be maximized to meet the growing global demand for pasta production. Precision agriculture (PA) could play a pivotal role in reaching this goal by correctly defining management zones (MZ) and optimizing the use of energy inputs. The aim of the work was to understand the relationship between MZ generated from observed yield data and those generated using a time series of Sentinel-derived vegetation indices (i.e. NDVI) obtained from satellite images and soil properties. For this purpose, two field trials of 10&#xa0;ha each, cultivated with durum wheat, were carried out in Southern Italy. The results suggested a better strategy for defining MZs by merging soil characteristics and temporal NDVI stability maps. The on-the-go technology used for soil resistivity mapping also represented an excellent tool for delineating stable and homogeneous areas within the fields and estimating soil properties. In particular, the soil clay content had a determining effect on the identification of homogeneous yield areas. However, the integration of historical NDVI data helped delineate MZs within each field. To validate this hypothesis, we integrated soil and NDVI data into a linear predictive model to predict grain yield at the field level. Our findings showed a good level of accuracy and a significant improvement in yield simulated values by combining soil with crop data (R<sup>2</sup> = 0.620; RMSE = 0.425). Further studies are needed to explore the potential of NDVI stability maps into a linear predictive model to predict grain yield at the field level.</p>

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Stability maps using historical NDVI images on durum wheat to understand the causes of spatial variability

  • 期刊论文

  • 发表日期：

    2025年1月28日

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  • E. Romano,
  • F. Fania,
  • I. Pecorella,
  • P. Spadanuda,
  • M. Roncetti,
  • D. Zullo,
  • G. Giuntoli,
  • C. Bisaglia,
  • A. Bragaglio,
  • S. Bergonzoli,
  • P. De Vita

摘要

Durum wheat (Triticum durum Desf.) yield should be maximized to meet the growing global demand for pasta production. Precision agriculture (PA) could play a pivotal role in reaching this goal by correctly defining management zones (MZ) and optimizing the use of energy inputs. The aim of the work was to understand the relationship between MZ generated from observed yield data and those generated using a time series of Sentinel-derived vegetation indices (i.e. NDVI) obtained from satellite images and soil properties. For this purpose, two field trials of 10 ha each, cultivated with durum wheat, were carried out in Southern Italy. The results suggested a better strategy for defining MZs by merging soil characteristics and temporal NDVI stability maps. The on-the-go technology used for soil resistivity mapping also represented an excellent tool for delineating stable and homogeneous areas within the fields and estimating soil properties. In particular, the soil clay content had a determining effect on the identification of homogeneous yield areas. However, the integration of historical NDVI data helped delineate MZs within each field. To validate this hypothesis, we integrated soil and NDVI data into a linear predictive model to predict grain yield at the field level. Our findings showed a good level of accuracy and a significant improvement in yield simulated values by combining soil with crop data (R2 = 0.620; RMSE = 0.425). Further studies are needed to explore the potential of NDVI stability maps into a linear predictive model to predict grain yield at the field level.