Modelling apparent nitrogen mineralization under field conditions using regression and artificial neural networks

R. ALVAREZ; H. STEINBACH

AGRONOMY JOURNAL

AMER SOC AGRONOMY

Año: 2011 p. 1159 - 1168

0002-1962

Soil nitrogen mineralization is an important source of nitrogen for grain crops but its estimation under field conditions is usually very difficult. Our objective was to develop models suitable for predicting nitrogen mineralization during the growing seasons of wheat (Triticum aestivum L.) and corn (Zea mays L.) under field conditions. Fifty-eight field experiments were performed with wheat, and 35 with corn, along three growing seasons, in which soil apparent nitrogen mineralization was estimated by the mass balance approach. Apparent nitrogen mineralized from decomposing residues (ANMR) or soil humic substances (ANMH) were estimated separately. Two empirical modeling techniques were tested, linear regression and artificial neural networks, using as independent variables or inputs some environmental variables. Both techniques allowed the development of suitable models for nitrogen mineralization prediction (R2> 0.68), but neural networks gave slightly better results. ANMR ranged from -42 to 64 kg N ha-1, increasing as residue mass and nitrogen concentration increased. An average ANMR of 15-16 kg N ha-1 was produced both during wheat and corn growing seasons. ANMH ranged from -80 to 328 kg N ha-1, being on average four times greater during corn growing cycle than during wheat season (127 vs. 34 kg N ha-1). ANMH decreased as initial mineral nitrogen content of the soil, remaining residue mass or fine particles content of the soil increased, and it was greater in soils of higher organic matter level and mineralization potential, as determined by an incubation test. Increases in temperature and rainfall also determine greater ANMH. The methodology developed for apparent nitrogen mineralization estimation may be applied to other crops and production regions.