An Original Experimental Design to Quantify and Model Net Mineralization of Organic Nitrogen in the Field.

Morvan, Thierry; Beff, Laure; Lambert, Yvon; Mary, Bruno; Germain, Philippe; Louis, Benjamin; Beaudoin, Nicolas

Improving the assessment and prediction of soil organic nitrogen (N) mineralization is essential: it contributes significantly to the N nutrition of crops and remains a major economic and environmental challenge. Consequently, a network of 137 fields was established in Brittany, France, to represent the wide diversity of soils and cultivation practices in this region. The experimental design was developed to measure net N mineralization for three consecutive years, in order to improve the accuracy of measuring it. Net N mineralization was quantified by the mineral N mass balance, which was estimated from March to October for a maize crop with no N fertilization. The effect of climate on mineralization was considered by calculating normalized time (ndays) and, then, calculating the N mineralization rate (Vn) as the ratio of the mineral N mass balance to normalized time. Strict screening of the experimental data, using agronomic and statistical criteria, resulted in the selection of a subset of 67 fields for data analysis. Mean Vn was relatively high (0.99 kg N ha−1 nday−1) over the period and varied greatly, from 0.62 to 1.46 kg N ha−1 nday−1 for the 10th and 90th percentiles, respectively. The upper soil layer (0–30 cm) was sampled to estimate its physical and chemical properties, particulate organic matter carbon and N fractions (POM-C and POM-N, respectively), soil microbial biomass (SMB), and extractable organic N (EON) determined in a phosphate borate extractant. The strongest correlations between Vn and these variables were observed with EON (r = 0.47), SMB (r = 0.45), POM-N (r = 0.43), and, to a lesser extent, the soil N stock (r = 0.31). Vn was also strongly correlated with a cropping system indicator (r = 0.39). A modeling approach, using generalized additive models, was used to identify and rank the variables with the greatest ability to predict net N mineralization.

BRITTANY (France); FRANCE; MINERALIZATION; EXPERIMENTAL design; CROPPING systems; SUBSET selection; TILLAGE; NITROGEN

Nitrogen (2504-3129), 2022, Vol 3, Issue 2, p197

2504-3129

Academic Journal

10.3390/nitrogen3020015