Narrowing the agronomic yield gap with improved nitrogen use efficiency: a modeling approach.

Improving nitrogen use efficiency (NUE) in the major cereals is critical for more sustainable nitrogen use in high-input agriculture, but our understanding of the potential for NUE improvement is limited by a paucity of reliable on-farm measurements. Limited on-farm data suggest that agronomic NUE (AE(N)) is lower and more variable than data from trials conducted at research stations, on which much of our understanding of AE(N) has been built. The purpose of this study was to determine the magnitude and causes of variability in AE(N) across an agricultural region, which we refer to as the achievement distribution of AE(N). The distribution of simulated AE(N) in 80 farmers' fields in an irrigated wheat system in the Yaqui Valley, Mexico, was compared with trials at a local research center (International Wheat and Maize Improvement Center; CIMMYT). An agroecosystem simulation model WNMM was used to understand factors controlling yield, AE(N), gaseous N emissions, and nitrate leaching in the region. Simulated AE(N) in the Yaqui Valley was highly variable, and mean on-farm AE(N) was 44% lower than trials with similar fertilization rates at CIMMYT. Variability in residual N supply was the most important factor determining simulated AE(N). Better split applications of N fertilizer led to almost a doubling of AE(N), increased profit, and reduced N pollution, and even larger improvements were possible with technologies that allow for direct measurement of soil N supply and plant N demand, such as site-specific nitrogen management.

Simulating the influence of vernalization, photoperiod and optimum temperature on wheat developmental rates.

BACKGROUND AND AIMS: Accurately representing development is essential for applying crop simulations to investigate the effects of climate, genotypes or crop management. Development in wheat (Triticum aestivum, T. durum) is primarily driven by temperature, but affected by vernalization and photoperiod, and is often simulated by reducing thermal-time accumulation using vernalization or photoperiod factors or limiting accumulation when a lower optimum temperature (T(optl)) is exceeded. In this study T(optl) and methods for representing effects of vernalization and photoperiod on anthesis were examined using a range of planting dates and genotypes. METHODS: An examination was made of T(optl) values of 15, 20, 25 and 50 degrees C, and either the most limiting or the multiplicative value of the vernalization and photoperiod development rate factors for simulating anthesis. Field data were from replicated trials at Ludhiana, Punjab, India with July through to December planting dates and seven cultivars varying in vernalization response. KEY RESULTS: Simulations of anthesis were similar for T(optl) values of 20, 25 and 50 degrees C, but a T(optl) of 15 degrees C resulted in a consistent bias towards predicting anthesis late for early planting dates. Results for T(optl) above 15 degrees C may have occurred because mean temperatures rarely exceeded 20 degrees C before anthesis for many planting dates. For cultivars having a strong vernalization response, anthesis was more accurately simulated when vernalization and photoperiod factors were multiplied rather than using the most limiting of the two factors. CONCLUSIONS: Setting T(optl) to a high value (30 degrees C) and multiplying the vernalization and photoperiod factors resulted in accurately simulating anthesis for a wide range of planting dates and genotypes. However, for environments where average temperatures exceed 20 degrees C for much of the pre-anthesis period, a lower T(optl) (23 degrees C) might be appropriate. These results highlight the value of testing a model over a wide range of environments.