An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery.

Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (MTCI) indicated the highest accuracy at estimating corn biomass during the growing season with the R2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian model averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices.

Long-term numerical modeling of nitrate leaching into groundwater under surface drip irrigation of corn.

Proper management of fertigation is necessary to deal with the harmful impacts of fertilizers. This research aimed to investigate the nitrate leaching rate into groundwater in different fertigation management under the climate change impact in drip irrigation of corn. For this purpose, HYDRUS-2D was calibrated by performing field experiments. Plant water requirement and rainfall were projected until 2050 using LARS-WG6 under the RCP85 scenario. Then, nitrate leaching up to groundwater at the depth of 5 m was simulated in the growing season of corn and the like until 2050 in three fertigation scenarios, including S1 (three regional fertigation splits with irrigation efficiency of 85%), S2 (weekly fertigation with irrigation efficiency of 85%), and S3 (optimum fertigation with irrigation efficiency of 100%). Finally, the annual nitrate leaching rate to groundwater and leached amount were compared in the studied scenarios. The results demonstrated that nitrate penetrated to the depth of 117 and 105 cm at the end of the first year in S1 and S2 scenarios, respectively. In these scenarios, nitrate will reach groundwater in 2031, but nitrate concentrations will not be the same. In the S3 scenario, the nitrate will reach a depth of 180 cm by 2050. Total leached nitrate to groundwater up to 2050 will be 1740, 1200, and zero kg/ha in S1, S2, and S3 scenarios, respectively. Based on the approach of this study, the vulnerability of groundwater to nitrate contamination in different agricultural areas can be evaluated, and appropriate strategies with minimum environmental impacts of fertilizer abuse can be selected accordingly.

A hybrid machine learning approach for estimating the water-use efficiency and yield in agriculture.

This paper introduces the narrow strip irrigation (NSI) method and aims to estimate water-use efficiency (WUE) and yield in apple orchards under NSI in the Miandoab region located southeast of Lake Urmia using a machine learning approach. To perform the estimation, a hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and seasons optimization (SO) algorithm was proposed. According to the irrigation and climate factors, six different models have been proposed to combine the parameters in the SO-ANFIS. The proposed method is evaluated on a test data set that contains information about apple orchards in Miandoab city from 2019 to 2021. The NSI model was compared with two popular irrigation methods including two-sided furrow irrigation (TSFI) and basin irrigation (BI) on benchmark scenarios. The results justified that the NSI model increased WUE by 1.90 kg/m3 and 3.13 kg/m3, and yield by 8.57% and 14.30% compared to TSFI and BI methods, respectively. The experimental results show that the proposed SO-ANFIS has achieved the performance of 0.989 and 0.988 in terms of R2 criterion in estimating WUE and yield of NSI irrigation method, respectively. The results confirmed that the SO-ANFIS outperformed the counterpart methods in terms of performance measures.

Linking plant and soil indices for water stress management in black gram.

Measurement of plant and soil indices as well as their combinations are generally used for irrigation scheduling and water stress management of crops and horticulture. Rapid and accurate determination of irrigation time is one of the most important issues of sustainable water management in order to prevent plant water stress. The objectives of this study are to develop baselines and provide irrigation scheduling relationships during different stages of black gram growth, determine the critical limits of plant and soil indices, and also determine the relationships between plant physiology and soil indices. This study was conducted in a randomized complete block design at the four irrigation levels 50 (I1), 75 (I2), 100 (I3 or non-stress treatment) and 125 (I4) percent of crop's water requirement with three replications in Urmia region in Iran in order to irrigation scheduling of black gram using indices such as canopy temperature (Tc), crop water stress index (CWSI), relative water content (RWC), leaf water potential (LWP), soil water (SW) and penetration resistance (Q) of soil under one-row drip irrigation. The plant irrigation scheduling was performed by using the experimental crop water stress index (CWSI) method. The upper and lower baseline equations as well as CWSI were calculated for the three treatments of I1, I2 and I3 during the plant growth period. Using the extracted baselines, the mean CWSI values for the three treatments of I1, I2 and I3 were calculated to be 0.37, 0.23 and 0.15, respectively, during the growth season. Finally, using CWSI, the necessary equations were provided to determine the irrigation schedule for the four growing stages of black gram, i.e. floral induction-flowering, pod formation, seed and pod filling and physiological maturity, as (Tc - Ta)c = 1.9498 - 0.1579(AVPD), (Tc - Ta)c = 4.4395 - 0.1585(AVPD), (Tc - Ta)c = 2.4676 - 0.0578(AVPD) and (Tc - Ta)c = 5.7532 - 0.1462(AVPD), respectively. In this study, soil and crop indices, which were measured simultaneously at maximum stress time, were used as a complementary index to remove CWSI constraints. It should be noted that in Urmia, the critical difference between the canopy temperature and air temperature (Tc - Ta), soil penetration resistance (Q), soil water (SW) and relative water content (RWC) for the whole growth period of black gram were - 0.036 °C, 10.43 MPa and 0.14 cm3 cm-3 and 0.76, respectively. Ideal point error (IPE) was also used to estimate RWC, (Tc - Ta) and LWP as well as to select the best regression model. According to the results, black gram would reduce its RWC less through reducing its transpiration and water management. Therefore, it can be used as a low-water-consuming crop. Furthermore, in light of available facilities, the farmer can use the regression equations between the obtained soil and plant indices and the critical boundaries for the irrigation scheduling of the field.

An analysis of optimal fertigation implications in different soils on reducing environmental impacts of agricultural nitrate leaching.

Excessive and incorrect use of nitrogen (N) fertilizers in agriculture leads to high nitrate leaching to groundwater and harmful effects on the environment. The main objective of this research was to optimize the N fertigation scheduling for a surface micro-irrigation system in different soils. N uptake by corn and its losses were investigated for two fertigation scheduling scenarios including regional recommendation scheduling with three fertigation events and a weekly application schedule. The fertigation scheduling was then optimized to achieve both environmental objectives (minimizing nitrate losses) and corn N requirements (maximizing N uptake sufficiency). For this purpose, the HYDRUS-2D model, simulating water flow and N transport in soil, was linked to an optimization algorithm. In both scenarios, N uptake by plant was not adequate at different stages of growth in all three soil types, especially in the sandy loam soil. Optimization produced a decrease in nitrate leaching and an increase in N uptake as well as fully supplied plant requirements at different stages of corn growth. Optimization framework presented in this study and optimum fertigation scheduling in various soil textures can be applicable as a guideline for operators of micro-irrigation systems which reduce nitrate leaching and increase N uptake sufficiency.

Evaluation of fertigation management impacts of surface drip irrigation on reducing nitrate leaching using numerical modeling.

The objective of this study was to investigate the impacts of fertigation strategies on nitrate leaching and its uptake into maize plants. Field experimental data were employed to calibrate a numerical model (HYDRUS 2D/3D) for a surface drip irrigation system in a sandy clay loam soil. The calibrated model was used to simulate nitrate plant uptake and its leaching in different fertigation scenarios based on various fertigation durations and different start times of fertigation. Finally, nitrogen plant uptake was compared with maize N requirement during growth stages in two fertigation frequency scenarios. These simulations were also performed in sandy loam soil. The results show that, if fertigation is done at the end of irrigation, nitrate leaching in shorter fertigation duration will be less than the leaching in longer fertigation duration. However, in the case of fertigation at the beginning of irrigation, the nitrate leaching is higher if the fertigation duration is short, and vice versa. Furthermore, reducing the number of fertigation events in the sandy clay loam soil increases the nitrate plant uptake. However, in the sandy loam soil, a lesser number of fertigation events reduce nitrate uptake.