Rice is an important cereal and drought stress is a critical abiotic stress that negatively influences the performance and productivity of rice crop, particularly under a changing climate scenario. The objectives of this study were to evaluate the impacts of drought stress on grain productivity and water use efficiency of rice cultivars and to assess the genotypic variability among the tested cultivars. Two irrigation treatments including a control and drought stress were applied to the experiments during 2018–2019 and 2019–2020. The statistical evaluation included a comparison of means, genotypic and phenotypic coefficients of variation, path analysis, correlation assessment, hierarchical clustering of tested cultivars and principal component analysis. The results indicated that drought stress negatively affected the grain productivity of the rice cultivars. The grain productivity of the cultivars decreased, ranging between 21–45% and 21–52% in the first and second season, respectively. Similarly, water use efficiency was significantly decreased ranging between 7–53% and 21–55% during the first and the second season, respectively. The broad-sense heritability for grain productivity was differed under control and drought stress treatment, indicating that the chances of the transfer of grain-productivity-related traits could be affected during selection for stress tolerance. The correlation assessment indicated that the intensity of association among the evaluated parameters was higher under the control treatment. A maximum direct effect was observed by water consumption (1.76) under control whereas, by water use efficiency (1.09) under drought stress treatment on grain productivity in path analysis. Considering the water use efficiency as a desired trait for selection in path analysis, a maximum direct effect was observed by grain productivity under the control (0.68) and under drought treatment (0.88). Hom Pathum and Pathum Thani−1 were identified as highly tolerant cultivars in the hierarchical clustering and principal component analysis. It was concluded that the results obtained for the assessment of drought stress on grain productivity, water use efficiency and genotypic variability among these cultivars could be utilized in selection program for stress tolerance and the stress tolerant cultivars could be used for sustaining grain productivity to reduce the impacts of climate change.

Nitrogen (N) deficiency can limit rice productivity, whereas the over- and underapplication of N results in agronomic and economic losses. Process-based crop models are useful tools and could assist in optimizing N management, enhancing the production efficiency and profitability of upland rice production systems. The study evaluated the ability of CSM–CERES–Rice to determine optimal N fertilization rate for different sowing dates of upland rice. Field experimental data from two growing seasons (2018–2019 and 2019–2020) were used to simulate rice responses to four N fertilization rates (N₃₀, N₆₀, N₉₀ and a control–N₀) applied under three different sowing windows (SD1, SD2 and SD3). Cultivar coefficients were calibrated with data from N₉₀ under all sowing windows in both seasons and the remaining treatments were used for model validation. Following model validation, simulations were extended up to N₂₄₀ to identify the sowing date’s specific economic optimum N fertilization rate (EONFR). Results indicated that CSM–CERES–Rice performed well both in calibration and validation, in simulating rice performance under different N fertilization rates. The d-index and nRMSE values for grain yield (0.90 and 16%), aboveground dry matter (0.93 and 13%), harvest index (0.86 and 7%), grain N contents (0.95 and 18%), total crop N uptake (0.97 and 15%) and N use efficiencies (0.94–0.97 and 11–15%) during model validation indicated good agreement between simulated and observed data. Extended simulations indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha⁻¹ (N₁₈₀), where the yield plateau was observed. Fertilization rates of 140, 170 and 130 kg N ha⁻¹ were identified as the EONFR for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. The model results suggested that N fertilization rate should be adjusted for different sowing windows rather than recommending a uniform N rate across sowing windows. In summary, CSM–CERES–Rice can be used as a decision support tool for determining EONFR for seasonal sowing windows to maximize the productivity and profitability of upland rice production.

A suitable nitrogen (N) application rate (NAR) and ideal planting period could improve upland rice productivity, enhance the soil water utilization, and reduce N losses. This study was conducted for the assessment and application of the EPIC model to simulate upland rice productivity, soil water, and N dynamics under different NARs and planting windows (PWs). The nitrogen treatments were 30 (N30), 60 (N60), and 90 (N90) kg N ha⁻¹ with a control (no N applied −N0). Planting was performed as early (PW1), moderately delayed (PW2), and delayed (PW3) between September and December of each growing season. The NAR and PW impacted upland rice productivity and the EPIC model predicted grain yield, aboveground biomass, and harvest index for all NARs in all PWs with a normalized good–excellent root mean square error (RMSEn) of 7.4–9.4%, 9.9–12.2%, and 2.3–12.4% and d-index range of 0.90–0.98, 0.87–0.94, and 0.89–0.91 for the grain yield, aboveground biomass, and harvest index, respectively. For grain and total plant N uptake, RMSEn ranged fair to excellent with values ranging from 10.3 to 22.8% and from 6.9 to 28.1%, and a d-index of 0.87–0.97 and 0.73–0.99, respectively. Evapotranspiration was slightly underestimated for all NARs at all PWs in both seasons with excellent RMSEn ranging from 2.0 to 3.1% and a d-index ranging from 0.65 to 0.97. A comparison of N and water balance components indicated that PW was the major factor impacting N and water losses as compared to NAR. There was a good agreement between simulated and observed soil water contents, and the model was able to estimate fluctuations in soil water contents. An adjustment in the planting window would be necessary for improved upland rice productivity, enhanced N, and soil water utilization to reduce N and soil water losses. Our results indicated that a well-calibrated EPIC model has the potential to identify suitable N and seasonal planting management options.

Coarse‐textured soils in central Minnesota cultivated with corn ( Zea mays L.) and soybean ( Glycine max L.) exhibit good productivity, however, are vulnerable to nitrate‐N leaching losses. In such circumstances, winter rye ( Secale cereale L.) as a cover crop may reduce nitrate‐N leaching by scavenging soil nitrogen (N) in late‐fall and early‐spring fallow period. The Environmental Policy Integrated Climate (EPIC) model was used for decadal‐scale (2010–2020) simulation of yield/biomass and nitrate‐N leaching in corn– (C–C) and corn–soybean/soybean–corn (C–Sb/Sb–C) rotations, with and without winter rye, under different fertilizer N rates applied to corn (0, 100, 200, 250, and 300 kg ha ⁻¹ ) on irrigated coarse‐textured soils in central Minnesota. Model efficiency calculated based on Nash–Sutcliffe coefficient, relative root mean square error, and R ² statistics indicate that EPIC assessment for calibration and validation treatments was excellent‐good for corn/soybean yield, and good‐satisfactory for rye biomass and NO ₃ ‐N leaching losses. Results indicate that N fertilizer rates up to 250 kg N ha ⁻¹ applied to corn had a positive impact on rye biomass; however, large crop‐rotation and climate‐induced variations were observed. Annual nitrate‐N leaching losses at maximum return to nitrogen rates at a 0.05 N price to crop value ratio for corn under C–C (250 kg N ha ⁻¹ ) and C–Sb/Sb–C (200 kg N ha ⁻¹ ) with no‐rye averaged 61.5, 47.4, and 41.8 kg ha ⁻¹ , while grain yield averaged 12.5, 12.3, and 4.0 t ha ⁻¹ for corn (C–C), corn (C–Sb/Sb–C), and soybean (C–Sb/Sb–C), respectively. Planting rye under these rotations gave annual average reductions in nitrate‐N losses relative to corresponding no‐rye treatments of 2.9 (4.7%), 3.4 (7.3%), and 6.5 kg ha ⁻¹ (15.6%), with rye N uptake of 10.3, 12.1, and 33.5 kg ha ⁻¹ ; and rye biomass production of 0.61, 0.74, and 2.0 t ha ⁻¹ , respectively. EPIC assessment indicates that winter rye as cover crop did not negatively impact the subsequent corn/soybean yield and proved to be an effective strategy for reducing nitrate‐N losses, particularly following the soybean crop.

The current study aimed to improve the nitrogen recovery efficiency (NRE) of maize under the application of balanced proportions of ammoniacal-nitrogen (NH₄⁺-N) and nitrate-nitrogen (NO₃⁻-N) (1:1), as well as to determine economic impacts of such fertilizers on maize productivity. A 3-year field study was carried out in Sahiwal, Punjab, Pakistan during the 2018–2020 growing seasons with autumn maize. In parallel, multi-location field experiments were conducted at farmer’s fields in Sahiwal during the same growing seasons with autumn maize. The trials compared the effects of different fertilizers like urea, calcium ammonium nitrate (CAN), Sarsabz nitrophos (NP), nitrphos plus (NP Plus), and di-ammonium phosphate (DAP) on maize growth and yield, as well their economic efficiency. The results revealed that the application of Sarsabz NP and CAN increased the maize grain yield by 30%, 13%, 15%, 19%, 15%, and 9% as compared to the control, NP + urea, NP Plus + CAN, NP Plus + urea, DAP + urea, and DAP + CAN, respectively. In addition, the combined application of NP and CAN increased the total N and NO₃⁻-N concentrations, whereas it decreased the NH₄⁺-N concentrations in the soils. By contrast, NP + CAN increased the NH₄⁺-N concentrations in maize leaves as compared to NO₃⁻-N concentrations. Overall, the NRE of maize plants and the net return were higher under combined application of NP and CAN. In conclusion, CAN fertilizer increased the soil NO₃⁻-N concentrations, which thus assimilated in the plants as NH₄⁺ and improved the NRE of maize. Improved NRE thus enhanced maize yield and resulted in the maximum net return as compared to all other fertilizer combinations.

Efforts to mitigate the nitrogen (N) footprint of maize production include using N‐fixing microbes (NFM) and/or microbial inhibitors. We quantified the effects of NFM, the nitrification inhibitor (NI) 2‐(N‐3,4‐dimethyl‐1H‐pyrazol‐1‐yl) succinic acid isomeric mixture, and the urease inhibitor (UI) N‐(n‐butyl) thiophosphoric triamide, each applied by itself or paired with another additive, on nitrous oxide (N₂O) emissions, nitrate (NO₃⁻) leaching, and crop performance in contrasting irrigated and rainfed maize systems over two growing seasons. We also used published emission factors to estimate indirect N₂O emissions from leached NO₃⁻ that can be converted to N₂O. Agronomic effects were relatively small; the NI + NFM treatment increased N use efficiency and grain yield and protein content in some cases by 11%–14% relative to a treatment receiving only urea. Most of the additive treatments reduced direct (in‐field) N₂O emissions, most consistently for treatments that contained NI which reduced emissions by 24%–77%. However, these beneficial effects were counteracted by increased NO₃⁻ leaching, which occurred most consistently with UI or NFM applied as single additives or with NI. In these treatments, NO₃⁻ leaching increased during at least one growing season, and at both sites, by factors of 2–7. In three site‐years, increased NO₃⁻ leaching with NFM and NI + NFM offset large reductions in direct N₂O, such that total direct + indirect N₂O emissions were not different from that in the urea only treatment. These unintended effects may have resulted from unfavorable rainfall timing, varying crop N demand, and declining additive effectiveness. Use of these soil additives requires caution and further study.