Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI < 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes.

Global nitrogen use efficiency (NUE) for cereal production is marginal and is estimated to be about 33%. Remote sensing tools have tremendous potential for improving NUE in crops through efficient nitrogen management as well as the identification of high-NUE genotypes. The objectives of this study were (i) to identify and quantify the variation in NUE across 24 winter wheat genotypes (Triticum aestivum L.) and (ii) to determine if the normalized difference vegetation index (NDVI) could characterize the variability in NUE across wheat genotypes. This study was conducted in 2010 and 2011 in the semi-arid climate of Northeastern Colorado across dryland and irrigated conditions. Our results indicate significant variation in the NUE among genotypes across two irrigation conditions. We observed a strong relationship between the NDVI and NUE—as PFP (partial factor productivity) and PNB (partial nitrogen balance)—across the 24 wheat genotypes under dryland conditions (average R² for PFP and PNB = 0.84) at Feekes growth stage 11.1, for site year II. However, poor association was observed under irrigated conditions (average R² for PFP and PNB = 0.29) at Feekes growth stage 3 to 4 for site year II. This study demonstrates the potential and limitations of active canopy sensing to successfully characterize the variability in NUE across wheat genotypes.

This study hypothesized that alternating fresh and saline irrigation with different dripper types would optimize water use while minimizing negative effects on soil bulk density (ρb) and maize growth. The field experiment was carried out to investigate the impact of the types of the dripper and the salinity of irrigation water rotation on the ρb, maize (Zea mays L.) growth, and yield using two kinds of drippers (turbo and spiral) and two levels of irrigation water with different salinity ratios (low, symbolized by L) and (high, symbolized by H). Irrigation water was added into three rotations (L, H), (H, L, H), and (L, H, L). Soil ρb increased by 22.63% under saline irrigation, while yield was 3.07% higher with turbo drippers compared to spiral drippers. The results displayed an increase in plant height, leaf area, and yield by using the (L, H, L) as compared to (L, H) and (H, L, H), respectively. These results suggest that alternating fresh and saline water could reduce freshwater usage by 50% while maintaining acceptable crop yields, making it a cost-effective solution for water-scarce regions.

Soil nitrogen (N) is a crucial nutrient for agricultural productivity and ecosystem health. The accurate and timely assessment of total soil N is essential for evaluating soil health. This study aimed to determine the impact of bootstrapping techniques on improving the predictive accuracy of indirect total soil N in conventional wheat fields in Al-Muthanna, Iraq. We integrated a novel methodological framework that integrated bootstrapped and non-bootstrapped total soil N data from 110 soil samples along with Landsat 9 imagery on the Google Earth Engine (GEE) platform. The performance of the proposed bootstrapping-enhanced random forest (RF) model was compared to standard RF models for soil N prediction, and outlier samples were analyzed to assess the impact of soil conditions on model performance. Principal components analysis (PCA) identified the key spectral reflectance properties that contribute to the variation in soil N. The PCA results highlighted NIR (band 5) and SWIR2 (band 7) as the primary contributors, explaining over 91.3% of the variation in soil N within the study area. Among the developed models, the log (B5/B7) model performed best in capturing soil N (R² = 0.773), followed by the ratio (B5/B7) model (R² = 0.489), while the inverse log transformation (1/log (B5/B7), R² = 0.191) exhibited the lowest performance. Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. This study established an efficient framework for improving predictive capacity in areas characterized by limited, low-quality, and incomplete spatial data, offering valuable insights for sustainable nitrogen management in arid regions dominated by monoculture systems.

Rationalization of irrigation water, as global warming, has negatively affected the availability of irrigation water. The field experiment was conducted for the purpose of studying the effect of the type of dripper and the salinity of irrigation water rotation on the bulk density and maize growth and yield using two types of drippers (turbo and spiral) and two levels of irrigation water with different salinity ratios (low, symbolized by L) and (high and symbolized by H). Irrigation water was added in three rotations (L, H), (H, L, H), and (L, H, L). The results showed an increase in bulk density values, giving the highest value for the spiral type compared to the turbo type. Our results indicated that water quality had a significant effect on soil bulk density values, with the highest bulk density values reaching for the (L, H), (H, L, H), (L, H, L), respectively. The results showed an increase in plant height, leaf area, and yield when using the (L, H, L), compared to (L, H) and (H, L, H), respectively. This study demonstrated the possibility of using high-salinity water, which is available in southern Iraq in rotation with fresh water to irrigate the maize crop.

As a result of Iraq’s scarcity of fresh water, there is a need to find alternative, non-traditional irrigation methods and technologies that would increase water use efficiency and reduce the negative impact of salinity on the tomato crop. The experiment was conducted in the field over two consecutive seasons in heavy soil using a drip irrigation system. The study employed two types of irrigation water with different salinity levels (low, symbolized as q1 = 0.8 ds m⁻¹) and high, symbolized as (q2 = 5.8 ds m⁻¹), and added in three ways: Q1 (q1), Q2 (q1, q2), and Q3 (q2). Two levels of organic matter (F2 and F3) were also used, along with a control treatment without the addition of F1. The study aimed to evaluate the effect of alternating fresh and saline water on tomato productivity, as well as to determine the impact and effectiveness of organic fertilizer in mitigating the negative effects of saline irrigation water and improving the chemical and physical properties of the soil. Statistical analysis showed that both irrigation water quality and amendments had a significant effect on the studied properties. The study year did not affect the overall characteristics of the study, but only the water conductivity and weighted diameter. The results showed an increase in water use efficiency, with averages of 20.7 and 21.13 kg ha⁻¹ mm⁻¹. when using fresh water and a high level of organic matter addition, sequentially. The water quality treatment Q2, combined with soil amendment F3, achieved the highest yield compared to the fresh water treatment Q1 combined with a control treatment (F1), reaching 4.321 and 3.993 kg plant⁻¹, respectively. This was achieved while conserving fresh water by 50% when using moderately saline drainage water with added amendments, without a significant decrease in tomato yield. Therefore, this study proposes adopting a strategy of using saline water with medium electrical conductivity as a partial alternative to low-salinity water, while incorporating organic amendments to ensure sustainable production in water-scarce regions.