Development of a Regional Soil Productivity Index Using an Artificial Neural Network Approach

Abstract

Soil productivity indices represent ratings of the potential plant biomass production of soils. Inductive approaches determine productivity based on inferred effects of soil properties on yield. Conversely, deductive approaches use yield information to estimate productivity. Our objective was to compare the performance of both types of productivity indices for assessing regional soil productivity for wheat (Triticum aestivum L.) yield in the Pampas. Soil data from soil surveys and interpolated climate information were utilized. Wheat yield data from a 40‐yr period and representing ?45 Mha were used. Inductive productivity indices showed a low correlation with observed yield (R2 < 0.45, P = 0.05). The best performance of deductive empirical methods was attained using a blind guess option, but soils could only be rated when yield data were available. Yield models based on the neural network approach had good performance (R2 = 0.614, root mean square error [RMSE] = 548 kg ha–1) and was used for regional productivity index development. This index could be extrapolated to soils for which yield data are not available, and its validation with yield averages was optimal (R2 = 0.728, P = 0.05). Regional high productivity was achieved for combinations of medium to high levels of soil organic C and soil available water storage capacity variables, which showed a positive interaction. This methodology for assessing soil productivity based on an empirical yield‐based model may be applied in other regions of the world and for different crops.

How does soil organic carbon mediate trade-offs between ecosystem services and agricultural production?

“…This is also associated with soil texture, which in this subregion is much finer. In the Pampean Region it was found that the available water storage capacity is positively correlated with soil productivity (De Paepe and Álvarez, 2013). In agreement with this, the highest water storage capacity (Fig.…”

An Approach to Comparing Different Land Evaluation Methods with NDVI

“…Additionally, it has been reported that the SI model can be used as a reference in determining the ability of new methods (O'Geen et al, 2008). Comparison of the agricultural quality classes determined using the PI model with the 40-year wheat yield values indicates that the correlation between the resultant PI model and the existing productivity potentials of the soil is low and the PI model is not suitable for use in qualified land (De Paepe and Alvarez 2013), and PI model is suitable found for determining the suitable areas for crop cultivation in mountainous regions (Li et al, 2013).…”

Modeling Chlorophyll <formula formulatype="inline"> <tex Notation="TeX">$a$</tex> </formula> Concentration Affected by Artificial Upwelling in Qiandao Lake

“…Nevertheless, MLR gives a clear analysis of the relationship between output and each single input [25]. MQR is another common method, which takes the interactions between input variables into account [26], thereby, making it a better fit for nonlinear systems. Different from MLR, MQR can give out more information about correlation between output and input, including quadratic terms.…”