Assessing soil organic carbon (SOC) at the field scale is crucial for efficient environmental and agronomic management, especially within the precision agriculture framework. This enables the implementation of crop and soil management strategies to enhance soil quality, increase carbon sequestration, and improve crop yields. However, the process of sampling and assessing SOC is resource-intensive, demanding both in time and labour. This aspect is particularly relevant in agronomic research, where the need to assess the spatial correlation structure of the investigated variables requires the collection of large datasets with georeferenced data and often measurements need to be repeated over time as in the case long-term field experiments - LTE. Methods for minimizing information loss while reducing the sampling scheme, such as spatial simulated annealing (SSA), are particularly valuable in the scope of SOC assessment, especially when faced with budget and time/labour constraints. Within the structure of the SSA method, two critical components can be identified: i) the inclusion of highly informative covariates for the primary variable (SOC); ii) the selection of the most appropriate variogram model for spatial variability assessment. Covariates strongly correlated with SOC, such as those obtained from ground penetrating radar (GPR) that can be collected at a higher spatial density compared to SOC data, along with a well-performing model, can significantly enhance the efficiency of the sampling scheme reduction process. We conducted a study using data from an agronomic field experiment, which included 71 georeferenced sampling locations and, through an iterative downsizing process utilizing SSA, we progressively reduced the number of sampling points by removing 10, 15, and 20 observations. The sampling scheme was refined according to two distinct variogram models: the spherical and Gaussian-Matern models, both for the primary variable (SOC) and the covariate GPR variable. This process allowed us to identify the optimal variogram model, which has a key role in maximizing the reduction of redundant points while preserving those with valuable information, and to assess the role of the covariate variable both in improving the optimization of the sampling scheme for SOC and in replacing the primary variable to optimize the sampling scheme. Finally, to assess the impact of sampling scheme reduction, a validation process was performed by estimating the dropped points by means of the remaining points using ordinary kriging and regression kriging and analysing the accuracy of such estimation. Our analysis demonstrated that it was possible to reduce the original sampling scheme by approximately 20%, equivalent to eliminating 15 sampling points out of 71, without compromising its predictive capability.
Optimization of sampling design for soil total organic carbon assessment in the precision agriculture framework: Impact of different variogram models and potentiality of ground penetrating radar (GPR) covariate information
De Benedetto, Daniela
;Stellacci, Anna Maria
2024-01-01
Abstract
Assessing soil organic carbon (SOC) at the field scale is crucial for efficient environmental and agronomic management, especially within the precision agriculture framework. This enables the implementation of crop and soil management strategies to enhance soil quality, increase carbon sequestration, and improve crop yields. However, the process of sampling and assessing SOC is resource-intensive, demanding both in time and labour. This aspect is particularly relevant in agronomic research, where the need to assess the spatial correlation structure of the investigated variables requires the collection of large datasets with georeferenced data and often measurements need to be repeated over time as in the case long-term field experiments - LTE. Methods for minimizing information loss while reducing the sampling scheme, such as spatial simulated annealing (SSA), are particularly valuable in the scope of SOC assessment, especially when faced with budget and time/labour constraints. Within the structure of the SSA method, two critical components can be identified: i) the inclusion of highly informative covariates for the primary variable (SOC); ii) the selection of the most appropriate variogram model for spatial variability assessment. Covariates strongly correlated with SOC, such as those obtained from ground penetrating radar (GPR) that can be collected at a higher spatial density compared to SOC data, along with a well-performing model, can significantly enhance the efficiency of the sampling scheme reduction process. We conducted a study using data from an agronomic field experiment, which included 71 georeferenced sampling locations and, through an iterative downsizing process utilizing SSA, we progressively reduced the number of sampling points by removing 10, 15, and 20 observations. The sampling scheme was refined according to two distinct variogram models: the spherical and Gaussian-Matern models, both for the primary variable (SOC) and the covariate GPR variable. This process allowed us to identify the optimal variogram model, which has a key role in maximizing the reduction of redundant points while preserving those with valuable information, and to assess the role of the covariate variable both in improving the optimization of the sampling scheme for SOC and in replacing the primary variable to optimize the sampling scheme. Finally, to assess the impact of sampling scheme reduction, a validation process was performed by estimating the dropped points by means of the remaining points using ordinary kriging and regression kriging and analysing the accuracy of such estimation. Our analysis demonstrated that it was possible to reduce the original sampling scheme by approximately 20%, equivalent to eliminating 15 sampling points out of 71, without compromising its predictive capability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
