Effi cient use of N in plant production is an essential goal in crop management. An experiment was performed at INTA Balcarce, Argentina during 3-yr to evaluate the eff ect of row spacing and N level on nitrogen use effi ciency (NUE) in no-till (NT) irrigated maize (Zea mays L.). Treatments consisted of a factorial combination of row width (70, 52 and 35 cm) and N rates (0 to 180 kg N ha −1 ). Nitrogen rate and narrow rows increased total dry matter (DM), grain yield, and N accumulation. Relative responses to narrow rows decreased as N availability increased. Th e NUE decreased with N rate and increased with narrow row spacing. Narrow rows increased NUE by 12 and 15% expressed as DM or grain yield per unit of available N, respectively. Physiological effi ciency decreased with N rate and was not aff ected by row spacing. Recovery effi ciency (RE) decreased with increasing N rate, and increased for the narrow row spacings. Th e eff ect of narrow rows on RE decreased as N availability increased. Narrow rows increased NUE largely as a result of increased RE. Th ese increments in RE could contribute to increase the profi tability of maize production and to diminish the risk of environmental pollution.

Core Ideas Traditional corn N diagnostic methods (pre‐plant nitrate N test and pre‐sidedress nitrate N test) only account for mineral N. Objective: to improve N diagnostic methods by considering N mineralization. Pre‐plant nitrate N test and pre‐sidedress nitrate N test were improved by anaerobic‐N (Nan) in areas with similar soil/climates. Models combining Nan, texture and temperature improved pre‐plant nitrate N test and pre‐sidedress nitrate N test in all areas. Current N diagnostic methods for corn (Zea mays L.) are often based on the nitrate nitrogen (NO3−–N) concentration before planting (pre‐plant nitrate test, PPNT) and nitrate nitrogen (NO3−–N) concentration at V6 stage (PSNT). These tests provide scant information on soil N mineralization during the growing season, which can supply a considerable proportion of corn N requirements. The objective of our study was to evaluate if in‐season N recommendations could be improved by inclusion of a N mineralization potential estimator. We conducted field experiments (n = 35) in three different areas and in two planting dates. At each site we evaluated PPNT, PSNT, and NH4–N released during anaerobic incubation (Nan), which were then related to corn yield in unfertilized plots (0N) and corn response to nitrogen fertilization (Nresp%) using multiple regression analysis. The sole incorporation of Nan to PPNT and PSNT models improved their capacity to predict corn yield in 0N plots and Nresp% only in areas with similar edaphic‐climatic characteristics. Independently of the geographical region, when PPNT and PSNT were combined with Nan, texture, and temperature, their capacity to predict yield in 0N plots was increased (PPNT: from R2 0.02–0.47; PSNT: from R2 0.09–0.53), as it was their capacity to estimate Nresp% (PPNT: from R2 0.06–0.23; PSNT: from R2 0.19–0.42). The inclusion of Nan can improve traditional N diagnostic models when it is combined with edaphic/climatic properties that account for the mineralization rate of this N pool.

Core Ideas Study aimed to predict field N mineralization (Nmin) from anaerobically incubated N (Nan). Nan did not predict Nmin in areas with contrasting edaphic‐climatic properties. Nmin was predicted by a model including Nan, temperature and rainfall. The nitrogen (N) released after a 7‐d anaerobic incubation (Nan) is a good estimator of the size of the soil N mineralizable pool. However, there is a lack of information on how soil properties and climate affect the apparent field N mineralization (Nmin) of this pool. The objective of our study was to develop and validate a simple model to estimate Nmin from Nan in corn (Zea mays L.) and wheat (Triticum aestivum L.) fields. To this end, we performed 100 field experiments where we measured Nmin, Nan, rainfall, temperature (TC), soil texture, pH, soil organic matter (SOM), and pre‐sowing mineral N concentration (Ninitial). We performed a stepwise analysis to develop a model to predict Nmin using data from 70 sites, while the rest of the data was saved for model validation. The Nan ranged from 16 to 94 mg kg–1 while Nmin ranged from 22 to 232 kg ha–1. There was a strong association between Nan and Nmin within regions with similar climate and edaphic properties. However, we could not fit a single significant model to estimate Nmin based solely on Nan to be used in all regions. By considering other variables besides Nan, we developed a model that allowed predicting Nmin independently from the site [Nmin = –252 + 12.3(TC) + 1.37(Nan) + 0.27(rainfall)] (R2 = 0.89, model validation R2 = 0.83). This model could be useful to adjust N fertilizer recommendations for corn and wheat, reducing the economic and environmental impact of fertilization.