Optimization of Nitrogen, Phosphorus, and Farmyard Manure Fertilizers Application Rates in Fodder Maize Production by Response Surface Methodology

IntroductionThe excessive use of chemical fertilizers is a leading cause of environmental pollution in the agriculture sector. Therefore, optimizing fertilizer application is a crucial approach to boost production while minimizing environmental harm. On the other hand, application of chemical fertilizers along with manure can be considered as the proper management system that led to reduce the amount of chemical fertilizers and adverse effects on environment and also improve nutrition for plants. Response-surface methodology is a powerful tool to optimize production resources which decreases cost and time of the experiments by reducing number of them. Therefore, the aim of the study was optimization of chemical fertilizers of nitrogen and phosphorus along with manure application in fodder maize production.Materials and MethodsThe field experiment was carried out according to box-benkhen design in Savojbelagh, where located in the west of Alborz province, over two years 2017 and 2018. The fifteen treatments were selected based on low and high levels of nitrogen (0 and 300 kg.ha-1), phosphorus (0 and 150 kg.ha-1), and manure (0 and 40 ton.ha-1). Three replications were considered in central points under box-benkhen design. The experiment was performed in two replications. Fodder yield, dry matter, nitrogen losses, and nitrogen use efficiency were the measured traits in the study. Stepwise regression model by fitting a full quadratic function were used to predict response variables. Treatments were optimized based on three scenarios: economic, environmental, and eco-environmental. Fodder yield, nitrogen losses, and nitrogen use efficiency were the primary factors used to determine the treatments in each respective scenario. In final, the optimum levels of treatments as independent variables were suggested to obtain target amounts of traits as dependent variables in each scenario. All analysis was performed using Minitab ver.16.Results and DiscussionThe results of regression analysis showed that the model had adequate accuracy to predict the studied properties (as dependent variables) based on experimental treatments (as independent variables). The calculated values of NRMSE illustrated that the model predicted data of dry matter, fodder yield, nitrogen losses, and nitrogen use efficiency with difference as 5.4, 2.3, 6.1, and 17.4 percent from measured data, respectively. Fodder yield was raised by increasing nitrogen fertilizer under all levels of phosphorus and manure. Increase slope of fodder yield by increasing nitrogen fertilizer amount under high levels of phosphor and manure was more than in low levels. The highest nitrogen loss was gained by application of 300 kg N.ha-1 along with no application of manure under nitrogen fertilizer × manure interaction. As findings, increasing the amount of nitrogen fertilizer caused to increase nitrogen losses and decrease in nitrogen use efficiency. Application of 242.42 kg N.ha-1 and 95.45 kg P.ha-1 along with 33.13 t manure.ha-1 was suggested under economic scenario that led to produce 105 t fodder.ha-1 with nitrogen losses of 150 kg.ha-1. In environmental scenario, no application of nitrogen and using 146.9 kg P.ha-1 with 21.82 t manure. ha-1 was proposed to gain 80.94 t fodder and 51.2 kg. ha-1 as nitrogen losses and 103.03 kg fodder.kg-1 N as nitrogen use efficiency. Under eco-environmental scenario that had priority in comparison with two other scenarios due to considering economical and environmental issues simultaneously; applying 78.79 kg N.ha-1, 150 kg P.ha-1and 8.48 t manure.ha-1 was suggested as optimum levels of treatments that led to obtain 90.87 t fodder.ha-1, 64.75 kg N losses.ha-1 and the highest value of nitrogen use efficiency as 116.4 kg fodder.ha-1 N.ConclusionConsidering the results, applying 78.79 kg N.ha-1, 150 kg P.ha-1and 8.48 t manure.ha-1 in eco-environmental scenario was suggested as optimum levels of treatments.