Nitrogen (N) fertilization in corn is often based on uniform rates and yield goals without considering the spatial and temporal variability of yield potential. It is well documented how uniform N ...rates lead to low N use efficiencies and environmental issues, resulting in reduced profit for farmers. Several site-specific approaches have been proposed to capture the yield spatial variability and adjust N rates to the actual crop nutrient requirements. The current study presents two original, site-specific N fertilization approaches, where two approaches at integrating crop simulation models, seasonal forecast and proximal sensing were tested across two corn seasons (2019 and 2020) in a field with significant spatial variability. In the first approach, top dressing N prescription maps were determined using the DSSAT crop model run with historical weather data, while in the second one, the maps were determined coupling DSSAT with seasonal forecasts and proximal sensing. Compared to the uniform fertilization treatment, both model-based approaches led to higher yields, N efficiency and gross margin in 2019 but not in 2020. The 2020 season was characterized by several major rainfall events, which were not present in the historical or seasonal forecast datasets. This inconsistency led to a substantial underestimation of the N leaching events in both model-based methodologies and consequently to higher-than-needed N fertilizer recommendations. Future studies should therefore focus on identifying ways to provide accurate seasonal estimates of extreme weather events to enable crop models to provide better N recommendations. In addition, the integration of proximal and remote sensing data into the crop model should be tested later in the season when spatial variability in crop N status peaks.
•Different weather datasets should be used to run a crop simulation model for N recommendations.•Historical and Seasonal forecasts did not consistently represent the timing and amounts of major rainfall events.•Proximal sensing should be integrated into crop models when spatial variability has its peak (V7 to V10 stages).•Autocalibration approaches for user-independent model calibrations should be integrated into the methodology.
Several remote sensing-based methods have been developed to apply site-specific nitrogen (N) fertilization in crops. They consider spatial and temporal variability in the soil-plant-atmosphere ...continuum to modulate N applications to the actual crop nutrient status and requirements. However, deriving fertilizer N recommendations exclusively from remote proximal and remote sensing data can lead to substantial inaccuracies and new, more complex approaches are needed.
Therefore, this study presents an improved approach that integrates crop modelling, proximal sensing and forecasts weather data to manage site-specific N fertilization in winter wheat. This improved approach is based on four successive steps: (1)
optimal
N supply is estimated through the DSSAT crop model informed with a combination of observed and forecast weather data; (2)
actual
crop N uptake is estimated using proximal sensing; (3) N prescription maps are created merging crop model and proximal sensing information, considering also the contribution of the soil N mineralisation; (4) N-Variable Rate Application (N-VRA) is implemented in the field. A VRA method based on DSSAT fed with historical weather data and a business-as- usual uniform fertilization were also compared.
The methods were implemented in a 23.4 ha field in Northern Italy, cropped to wheat and characterized by large soil variability in texture and organic matter content. Results indicated that the model-based approaches consistently led to higher yields, agronomic efficiencies and gross margins than the uniform N application rate. Furthermore, the proximal sensing-based approach allowed capturing of the spatial variability in crop N uptake and led to a substantial reduction of the spatial variability in yield and protein content. This study grounds the development of web-based software as a friendly tool to optimize the N variable rate application in winter cereals.
Soil temperature is a key driver of several physical, chemical, and biological processes. The Environmental Policy Integrated Climate (EPIC) is a comprehensive ecosystem model that simulates soil ...temperature dynamics using a cosine function approach driven by daily air temperature and average annual soil temperature at damping depth, which may erroneously predict lower soil temperatures in winter. A new cosine model and a pseudo-heat-transfer model were therefore developed and implemented for simulating soil temperature. The two methods were evaluated by comparing simulated daily soil temperatures with observed data at 24 study sites. Results showed that the two new methods had similar performance and the better statistical results obtained with these new methods demonstrated the ability to better predict the soil temperature for a wide range of pedoclimatic conditions, land management, and land uses. The main reason for the improved performance was due to a better prediction of soil temperature during the winter period.
•Original EPIC approach gives less than optimal results in cold weather.•Improved cosine function and new pseudo heat transfer added to the EPIC model.•Both methods improved the performance in simulating soil temperature.•Use of the pseudo heat transfer suggested in sites with snow cover.•Better results without calibration proved the robustness of improved cosine function.
Soil N mineralisation is the process by which organic N is converted into plant-available forms, while soil N immobilisation is the transformation of inorganic soil N into organic matter and ...microbial biomass, thereafter becoming bio-unavailable to plants. Mechanistic models can be used to explore the contribution of mineralised or immobilised N to pasture growth through simulation of plant, soil and environment interactions driven by management.
Our objectives were (1) to compare the performance of three agro-ecosystems models (APSIM, DayCent and DairyMod) in simulating soil N, pasture biomass and soil water using the same experimental data in three diverse environments (2), to determine if tactical application of N fertiliser in different seasons could be used to leverage seasonal trends in N mineralisation to influence pasture growth and (3), to explore the sensitivity of N mineralisation to changes in N fertilisation, cutting frequency and irrigation rate.
Despite considerable variation in model sophistication, no model consistently outperformed the other models with respect to simulation of soil N, shoot biomass or soil water. Differences in the accuracy of simulated soil NH4 and NO3 were greater between sites than between models and overall, all models simulated cumulative N2O well. While tactical N application had immediate effects on NO3, NH4, N mineralisation and pasture growth, no long-term relationship between mineralisation and pasture growth could be discerned. It was also shown that N mineralisation of DayCent was more sensitive to N fertiliser and cutting frequency compared with the other models.
Our results suggest that while superfluous N fertilisation generally stimulates immobilisation and a pulse of N2O emissions, subsequent effects through N mineralisation/immobilisation effects on pasture growth are variable. We suggest that further controlled environment soil incubation research may help separate successive and overlapping cycles of mineralisation and immobilisation that make it difficult to diagnose long-term implications for (and associations with) pasture growth.
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•We compare plant, soil and nutrient outputs of APSIM, DayCent and DairyMod.•We examined whether seasonal N fertilisation influenced mineralisation.•No model was consistently more reliable in simulating measured variables.•Seasonal climates had more influence on mineralisation than tactical N application.•Sensitivity of N mineralisation to fertiliser was generally greatest in DayCent.
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