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The profitability of maize cultivation depends on the production yield of this crop. This paper presents the analysis of the yield and profitability of maize cultivation in three crop farms of different sizes in Romania. From the analysis carried out, a number of results are obtained, which lead to the conclusion that in order to reach the profitability threshold, the production in the 60 ha farm would have to be at least 133.26 tons of maize in 2019, at least 85.83 tons of maize in 2020, and at least 165.56 tons of maize in 2021; in the 600 ha farm, the production in 2019 would have to be at least 2.350.23 tons of maize in 2019, 2,198.44 tons of maize in 2020, and 3,172.33 tons of maize in 2021; and in the 3,000 ha farm, the production should have been at least 4,157.75 tons of maize in 2019, 4,424.70 tons of maize in 2020, and 6,284.46 tons of maize in 2021. These productions were made only by the farm of 3,000 ha, which had a constant profitability of the maize crop, exceeding the profitability threshold every year, as follows: in 2019 with 2,216.85 tons of maize, in 2020 with 2,738.60 tons of maize and in 2021 with 3,887.34 tons. On farms of 60 ha and 600 ha, the profitability threshold could not be reached, in most cases, the major causes being of agropedoclimatic and economic nature. The conclusion of the analysis carried out was that, in order to have a constant profitability of the maize crop, it is necessary to counteract the drought by introducing irrigation to this crop, reducing production expenses and increasing the value of subsidies, so that the gross product highlights the profitability of the maize crop. [ABSTRACT FROM AUTHOR]

A field experiment was conducted in Al-Muthanna Governorate on the banks of the Al-Suwer River in Iraq in the spring of 2017. The objective of this study was to know the effect of adding humic and fulvic fertilizers and zinc sulfate fertilizers on the availability of zinc and phosphorous in the soil and plants and the yield of maize crops. Four treatments of humic and fulvic fertilizers were used: Without adding acid (H0), spraying at a concentration of 10 mL/L after 40 and 80 days of planting (H1), soaking the seeds at a concentration of 20 mL/L24 h before planting (H2), and soaking seeds at a concentration of 20 mL/Lplus spraying at a concentration of 10 mL/L(H3). Furthermore, three levels of zinc sulfate fertilizer were used after 40 and 80 days of planting: 0 mL/L (Z0), 30 mL/L (Z1), and 60 mL/L (Z2). The experimental design of this study was a Randomized Complete Block Design (RCBD) with three replicates/treatments. Soil and plant samples were collected at the end of the experiment. The results showed the superiority of the treatments H3Z2 and H3Z1, suggesting the dependence on the availability of zinc and phosphorus in the soil that impacted its content in the plant, dry matter, and grain weight. The H3Z2 and H3Z1 treatments achieved 0.683 and 0.678 mg Zn/kg soil and 0.241 and 0.230 kg Zn/ha and 26.25, 24.02 mg P/kg soil and 22.49 and 21.51 kg P/ha and 10.19 and 9.75 mg Mg/kg soil, and 7.712 and 7.632 kg Mg/ha, respectively. [ABSTRACT FROM AUTHOR]

The low availability of high-quality meteorological data resulted in the development of synthetic meteorological data generated by satellite or data interpolation, which are available in grids with varying spatio-temporal resolution. Among these different data sources, NASA/POWER and DailyGridded databases have been applied for crop yield simulations. The objective of this study was to evaluate the performance of these two datasets, in different time scales (daily, 10-day, monthly, and annual), as input data for estimating potential (YP) and attainable (YA) maize yields, using the FAO Agroecological Zone crop simulation model (FAO-AEZ), properly calibrated and validated. For that, daily weather data from ten Brazilian locations were collected and compared to the data extracted from NASA/POWER and DailyGridded systems and later applied to estimate the potential and attainable maize yields. DailyGridded data showed a better performance than NASA/POWER for all weather variables and time scales, with confidence index (C) ranging from 0.52 to 0.99 for the former and from 0.09 and 0.99 for the latter. As a consequence of that, DailyGridded data was better than NASA/POWER to estimate maize yields with estimates close to those obtained with observed data, with a lower mean absolute errors (< 30 kg ha−1) and a higher confidence index (C = 0.99). [ABSTRACT FROM AUTHOR]

Abstract: We studied the effect of prospective climate change upon crop yield, and related water footprint of maize (Zea mays L.) for a relevant case study area in the Po valley of Northern Italy. To simulate maize production we used a cropping system simulation model CropSyst, which we set up and validated by way of crop yield data during 2001–2010. We then calculated the present water footprint (green, blue) of maize in the area, defined as the absolute and specific (per kg yield) amount of water evapotranspired during growing season, under three irrigation scenarios, namely (i) no irrigation, (ii) manual irrigation at fixed dates, and (iii) automatic irrigation on demand. We then evaluated the effects of prospective climate change upon maize production until mid-century (2045–2054), and we quantified the water footprint therein. We considered climate variations with focus upon temperature, precipitation, and CO2. First, we assessed maize yield and water footprint sensitivity to potential changes of these weather variables. We then fed the maize yield model with properly downscaled climate projections (storyline A2, business as usual) from global circulation models (GCM), included within the board of the Intergovernmental Panel of Climate Change, IPCC, and with those from a local scenario LOC, obtained by projecting recently observed local climate trends (1975–2010). Under the worst, more likely future scenarios of increasing temperature and decreasing precipitation, crop yield decreased and water footprint, especially blue, increased, due to increased evapotranspiration, higher irrigation demand, and lower final yield. Increase of CO2, albeit possibly increasing water use efficiency, seemed not to affect the water footprint noticeably. A possible increase of precipitation as projected by some GCMs, may partly make up for the increase of temperature, especially under a no, or little irrigation scenario, further diminishing the blue water footprint. Uncertainty in future precipitation has the greatest impact in scenarios projecting maize yield and water footprint. Our study provides hints as to how one can (i) evaluate the amount of water required to cultivate maize or other crops, and virtually traded when such crops are sold or bought, (ii) evaluate the impact of climate change upon water footprint and virtual water trade, and (iii) benchmark objectively adaptation strategies for agricultural systems with an eye on least water consumption. [Copyright &y& Elsevier]

Abstract: Crop growth simulation models of varying complexity have been developed for predicting the effects of soil, water and nutrients on grain and biomass yields and water productivity of different crops. These models are calibrated and validated for a given region using the data generated from field experiments. In this study, a water-driven crop model AquaCrop, developed by FAO was calibrated and validated for maize crop under varying irrigation and nitrogen regimes. The experiment was conducted at the research farm of the Water Technology Centre, IARI, New Delhi during kharif 2009 and 2010. Calibration was done using the data of 2009 and validation with the data of 2010. Irrigation applications comprised rainfed, i.e. no irrigation (W1) irrigation at 50% of field capacity (FC) (W2) at 75% FC (W3) and full irrigation (W4). Nitrogen application levels were no nitrogen (N1), 75kgha−1 (N2) and 150kgha−1 (N3). Model efficiency (E), coefficient of determination (R 2), Root Mean Square error (RMSE) and Mean Absolute Error (MAE) were used to test the model performance. The model was calibrated for simulating maize grain and biomass yield for all treatment levels with the prediction error statistics 0.95< E <0.99, 0.29