Abstract: Considering intensive agricultural management practices and environmental conditions, the LandscapeDNDC model was applied for simulation of yields, N2O emission and nitrate leaching from major upland crops and temperate deciduous forest of the Haean catchment, South Korea. Fertilization rates were high (up to 314 kg N ha(-1) year(-1)) and resulted in simulated direct N2O emissions from potato, radish, soybean and cabbage fields of 1.9 and 2.1 kg N ha(-1) year(-1) in 2009 and 2010, respectively. Nitrate leaching was identified as the dominant pathway of N losses in the Haean catchment with mean annual rates of 112.2 and 125.4 kg N ha(-1) year(-1), causing threats to water quality and leading to substantial indirect N2O emissions of 0.84 and 0.94 kg N ha(-1) year(-1) in 2009 and 2010 as estimates by applying the IPCC EF5. Simulated N2O emissions from temperate deciduous forest were low (approx. 0.50 kg N ha(-1) year(-1)) and predicted nitrate leaching rates were even negligible (≤0.01 kg N ha(-1) year(-1)). On catchment scale more than 50% of the total N2O emissions and up to 75% of nitrate leaching originated from fertilized upland fields, only covering 24% of the catchment area. Taking into account area coverage of simulated upland crops and other land uses these numbers agree well with nitrate loads calculated from discharge and concentration measurements at the catchment outlet. The change of current agricultural management practices showed a high potential of reducing N2O emission and nitrate leaching while maintaining current crop yields. Reducing (39%) and splitting N fertilizer application into 3 times was most effective and lead to about 54% and 77% reducing of N2O emission and nitrate leaching from the Haean catchment, the latter potentially contributing to improved water quality in the Soyang River Dam, which is the major source of drinking water for metropolitan residents.

Abstract: • ARMOSA model simulated a maize ideotype with drought adaptation under climate change. • The ideotype needs less water for higher yield compared to current hybrids. • Higher production involves more crop residues that enhance soil C sequestration. • Soil organic C may generally decrease and N leaching will increase in sandy soil. The expected climate change will affect the maize yields in view of air temperature increase and scarce water availability. The application of biophysical models offers the chance to design a drought-resistant ideotype and to assist plant breeders and agronomists in the assessment of its suitability in future scenarios. The aim of the present work was to perform a model-based estimation of the yields of two hybrids, current vs ideotype, under future climate scenarios (2030–2060 and 2070–2100) in Lombardy (northern Italy), testing two options of irrigation (small amount at fixed dates vs optimal water supply), nitrogen (N) fertilization (300 vs 400 kg N ha− 1), and crop cycle durations (current vs extended). For the designing of the ideotype we set several parameters of the ARMOSA process-based crop model: the root elongation rate and maximum depth, stomatal resistance, four stage-specific crop coefficients for the actual transpiration estimation, and drought tolerance factor. The work findings indicated that the current hybrid ensures good production only with high irrigation amount (245–565 mm y− 1). With respect to the current hybrid, the ideotype will require less irrigation water (− 13%, p < 0.01) and it resulted in significantly higher yield under water stress condition (+ 15%, p < 0.01) and optimal water supply (+ 2%, p < 0.05). The elongated cycle has a positive effect on yield under any combination of options. Moreover, higher yields projected for the ideotype implicate more crop residues to be incorporated into the soil, which are positively correlated with the SOC sequestration and negatively with N leaching. The crop N uptake is expected to be adequate in view of higher rate of soil mineralization; the N fertilization rate of 400 kg N ha− 1 will involve significant increasing of grain yield, and it is expected to involve a higher rate of SOC sequestration.

Abstract: The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.