Spatiotemporal Response of Maize Yield to Edaphic and Meteorological Conditions in a Saline Farmland

Abstract

Spatiotemporal variability of crop production strongly depends on soil heterogeneity, meteorological conditions, and their interaction. Canopy reflectance can be used to describe crop status and yield spatial variability. The objectives of this work were to understand the spatiotemporal variability of maize (Zea mays L.) yield using ground-based reflectance acquisitions in a salinity-and water-stress-affected 21-ha field beside the Venice Lagoon, Italy. Intra-and interannual reflectance variations were analyzed across the entire field and at each map cell with time to understand how the different soil-related stresses (i.e., salinity and water) arise under different meteorological conditions. The results show that the normalized difference vegetation index (NDVI) acquired during the maize flowering and kernel maturation stages (during the three growing seasons of 2010, 2011, and 2012) effectively described yield spatiotemporal variability. In particular, stressed areas exhibited the smallest changes in NDVI during a single growing season. Soil salinity and water stress were responsible for approximately 44% of the intra-annual NDVI change. When multiyear NDVI data are compared, areas affected by soil salinity show the smallest temporal variability. Nevertheless, areas that are slightly saline and constantly affected by water stress could not be distinguished from highly saline areas. Multiyear reflectance data can be a useful tool to characterize areas where soil salinity is the main factor limiting crop production. In areas where several plant stresses occur simultaneously every year, the proposed approach could be used to guide precision irrigation to make adjustments for within-field leaching requirement and/or irrigation needs.

Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA

“…2007). Indeed, as also observed in other regions Scudiero et al, 2014), differences in reflectance between consecutive years were remarkable. Consequently, remote sensing data from a single (random) year should not be used when trying to map salinity, as the risk of producing poor results is clear.…”

“…4) should therefore be considered fairly high. For comparison, Scudiero et al (2014) studied the 3-year temporal variability of corn reflectance in an area characterized by contrasting soils and affected by multiple stress types, including salinity and water stress. They observed that NDVI temporal variability could be used to identify (qualitatively) saline areas, but could only account for a small amount of its variations (R 2 = 0.11) due to the biasing effects of other abiotic stresses constantly occurring over time.…”

“…Nevertheless, previous studies showed that landscape features that are relatively stable in time (such as soil salinity) can be emphasized using multi-year reflectance data . In fact, using multiple-year satellite data generally improves the reflectance-salinity relationships compared to those observed for a single-year (Lobell et al, 2007Furby et al, 2010;Dang et al, 2011;Scudiero et al, 2014). Scudiero et al (2014) showed that that areas affected by soil salinity are generally characterized by lower temporal variability of canopy reflectance than areas with healthy vegetation, or areas suffering from other stresses that are less stable in time (e.g., water stress, pests…).…”

“…In fact, using multiple-year satellite data generally improves the reflectance-salinity relationships compared to those observed for a single-year (Lobell et al, 2007Furby et al, 2010;Dang et al, 2011;Scudiero et al, 2014). Scudiero et al (2014) showed that that areas affected by soil salinity are generally characterized by lower temporal variability of canopy reflectance than areas with healthy vegetation, or areas suffering from other stresses that are less stable in time (e.g., water stress, pests…). The research of Lobell et al (2010) indicated that the multiyear analysis of MODIS (National Aeronautics and Space Administration Agency, USA) reflectance integrated by information on crop cover, could explain large portions (up to 53%) of the spatial variability of soil salinity in the Red River Valley, North Dakota and Minnesota, USA.…”

“…Canopy reflectance has been used to represent crop status throughout the growing season and to predict yield (Mulla, 2013). The intra-annual variations in crop reflectance depend on factors influencing plant growth through each year, and are, therefore, a means of monitoring vegetation health rather than discriminating between different stress types (Scudiero et al, 2014). Nevertheless, previous studies showed that landscape features that are relatively stable in time (such as soil salinity) can be emphasized using multi-year reflectance data .…”