Using a nitrogen mineralization index will improve soil productivity rating by artificial neural networks

ABSTRACT

In the Pampas, nitrogen fertilization rates are low and soil organic matter impacts crop yield. Wheat (Triticum aestivum L.) yield was related to total soil nitrogen (total N) and to nitrogen mineralization potential (mineralized N) to determine whether the effects of organic matter may be attributed to its capacity to act as a nitrogen source or to the improvement of the soil physical condition. Data of 386 sites from throughout the region comprised in a recent soil survey were used, in which climate and soil properties to 1 m depth were determined. Artificial neural networks were applied for total N and mineralized N estimation using climate and soil variables as inputs (R² = 0.59–0.70). The models allowed estimating total N and mineralizable N at county scale and related them to statistical yield information. Neural networks were also used for yield prediction. The best productivity model fitted (R² = 0.85) showed that wheat yield could be predicted by rainfall, the photothermal quotient, and mineralized N. The soil organic matter effect on crop yield seems to be mainly related to its nitrogen mineralization capacity. Using mineralized N as predictor would be a valuable tool for rating soil productivity.

KEYWORDS:

• Artificial neural networks
• soil nitrogen mineralization
• soil organic matter
• soil productivity
• wheat yield

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Consejo Nacional de Investigaciones Científicas y Técnicas [Proyect 084, period 2014-2016]; Universidad de Buenos Aires [Proyect 20020130100484BA].