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: A field experiment was conducted in Southern Italy to evaluate the effects of different water quality and fertilizers on yield performance of tomato crop. In mineral nitrogen (N) fertilizer and irrigation with fresh water (Electrical Conductivity, EC, = 0.9 dS m⁻¹) (FWF); mineral N fertilizer and irrigation with saline water (EC = 6.0 dS m⁻¹) (SWF); municipal solid waste (MSW) compost and irrigation with fresh water (EC = 0.9 dS m⁻¹) (FWC); MSW compost and irrigation with saline water (EC = 6.0 dS m⁻¹) (SWC). At harvest, weight and number of fruits and refractometric index (°Brix) were measured, total and marketable yield and dry matter of fruit were calculated. The results indicated that MSW compost, applied as amendment, could substitute the mineral fertilizer. In fact, in the treatments based on compost application, the tomato average marketable yield increased by 9% compared with treatments with mineral fertilizer. The marketable yield in the SWF and SWC treatments (with an average soil EC in two years to about 3.5 dS m⁻¹) decreased respectively of 20 and 10%, in respect to fresh water treatments. At the end of the experiment, application of compost significantly decreased the sodium absorption rate (SAR) of SWC treatment in respect of SWF (−29.9%). Significant differences were observed among the four treatments both on soil solution cations either exchangeable cations. In particular compost application increased the calcium (Ca) and potassium (K) contents in saturated soil paste respect to the SWF ones (31.4% and 59.5%, respectively). At the same time saturated soil paste sodium (Na) in SWC treatment recorded a decrease of 17.4% compared to SWF.

Abstract: Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2].