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Title: Delineation of site-specific management units in a saline region at the Venice Lagoon margin, Italy, using soil reflectance and apparent electrical conductivity

item SCUDIERO, ELIA - University Of Padua
item TEATINI, PIETRO - University Of Padua
item Corwin, Dennis
item DEIANA, RITA - University Of Padua
item BERTI, ANTONIO - University Of Padua
item MORARI, FRANCESCO - University Of Padua

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/30/2013
Publication Date: 11/30/2013
Citation: Scudiero, E., Teatini, P., Corwin, D.L., Deiana, R., Berti, A., Morari, F. 2013. Delineation of site-specific management units in a saline region at the Venice Lagoon margin, Italy, using soil reflectance and apparent electrical conductivity. Computers and Electronics in Agriculture. 99:54-64. doi: 10.1016/j.compag.2013.08.023.

Interpretive Summary: Crop yield varies within fields due to nonuniformity of a number of factors including climate, pests, disease, management, topography, and soil. Conventional farming manages a field uniformly; as a result, conventional farming tends to wastes resources and money, and tends to detrimentally impact the environment. One way of handling crop yield variability in a cost and resource effective manner is to divide a field into management units based on the observed yield and soil variability so that each unit can be treated similarly in order to optimize yield, resource utilization, and profitability, and minimize detrimental environmental impacts. These site-specific management units or SSMUs are a key component of precision agriculture. It was the objective of this study to build on previous work for delineating SSMUs with a single sensor by using a combination of sensors that provides complementary information. Two sensors were used to define SSMUs: remote imagery (normalized difference vegetation index; NDVI) and apparent soil electrical conductivity (ECa). This study provided strong evidence for the need to use multiple-sensor platforms to characterize more extensively the spatial variability of soil properties influencing yield as a means of making maps of SSMUs. Maps of the SSMUs provide producers, extension specialist, agricultural consultants, and NRCS staff with information for variable-rate technology (e.g., site-specific fertilizer and irrigation water application).

Technical Abstract: Site-specific crop management utilizes site-specific management units (SSMUs) to apply inputs when, where, and in the amount needed to increase food productivity, optimize resource utilization, increase profitability, and reduce detrimental environmental impacts. It is the objective of this study to demonstrate the delineation of SSMUs using geospatial apparent soil electrical conductivity (ECa) and bare-soil reflectance measurements. The study site was a 21-ha field at the southern margin of the Venice Lagoon, Italy, which is known to have considerable spatial variability of soil properties influencing crop yield. Maize (Zea mais L.) yield maps from 2010 and 2011 showed high spatial heterogeneity primarily due to variation in soil-related factors. Approximately 53% of the spatial variation in maize yield was successfully modeled according to the variability of four soil properties: salinity, texture, organic carbon content, and bulk density. The spatial variability of these soil properties was characterized by the combined use of intensive geospatial ECa measurements and bare-soil normalized difference vegetation index (NDVI) survey data. On the basis of the relationships with these soil properties, ECa and NDVI were used to divide the field into five SSMUs using fuzzy c-means clustering: one homogeneous with optimal maize yield, one unit affected by high soil salinity, one characterized by very coarse texture (i.e., sandy paleochannels), and two zones with both soil salinity and high organic carbon content. Yield monitoring maps provide valuable spatial information, but do not provide reasons for the variation in yield. However, even in cases where measurements like ECa and bare-soil NDVI are not directly correlated to maize yield, their combined use can help classify the soil according to its fertility. The identification of areas where soil properties are fairly homogeneous can help managing diverse soil-related issues optimizing resource use, lowering costs, and increasing soil quality.