Journal of Agronomy and Crop Science最新文献

Climate change and global warming have significantly reduced the availability of irrigation water supplied to sugar beet crops in semi-arid environments. Identifying and recommending drought-tolerant sugar beet cultivars in water-limited regions is essential for helping growers maintain stable and sustainable yields. Twenty-nine sugar beet genotypes, including 25 germplasm accessions and four commercial hybrids, were examined under moderate and severe drought stress conditions in a 2-year experiment to determine the most drought-tolerant genotypes and the stress indices that could be used to screen drought tolerance. Twelve drought tolerance indices were estimated for each genotype based on white sugar yield under non-stress (Yp), moderate (Ys1), and severe drought stress (Ys2) conditions. A significant drought stress response variation was observed in the tested genotype under both stress conditions. Based on drought tolerance indices, namely mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), yield index (YI), yield stability index (YSI), drought index (DI), relative drought index (RDI), harmonic mean (HM), stress susceptibility index (SSI), and tolerance index (TOL) as well as principal component analysis, the most drought-tolerant genotypes were identified. Sugar beet genotypes PI590861, PI590855, PI590851, PI590799, PI578081, and PI590775 were determined to be the most drought tolerant. The significant and positive correlation of white sugar yield under Ys1 and Ys2 conditions with MP, GMP, STI, YI, YSI, HM, DI, and RDI showed that they could be used as the most suitable indices to screen drought-tolerant genotypes. The selected individual genotypes with drought tolerance can be used to develop higher-yielding cultivars for drought-prone environments.

This study assessed the DSSAT/CERES-Maize model's ability to simulate daily and cumulative ET, water productivity (WP) and water use efficiency (WUE) for maize in tropical conditions. Observed ET data were obtained using the Bowen ratio (BREB) method in Piracicaba-SP, and the soil water balance (SWB) method in Serra Talhada-PE. Simulations used the FAO-56 Penman-Monteith (ET_PM) or Priestley–Taylor (ET_PT) equations for ET, combined with the Ritchie (R-2) or Suleiman–Ritchie (S–R) methods for soil evaporation. The model was able to simulate daily and cumulative ET (Piracicaba irrigated: RMSE = 1.13–1.75 mm d⁻¹, d = 0.69–0.89 and bias = 4.92%–25.81%; Piracicaba rainfed: RMSE = 1.70–2.02 mm d⁻¹, d = 0.46–0.73 and bias = 21.10%–35.37%; Serra Talhada irrigated: RMSE = 0.95–1.42 mm d⁻¹ and d = 0.45–0.75 and bias = −20.74%–3.19%), daily ET by phenological phase (Piracicaba irrigated: RMSE = 1.60–3.18 mm d⁻¹ and d = 0.18–0.78; Piracicaba rainfed: RMSE = 1.59–2.27 mm d⁻¹ and d = 0.14–0.73; Serra Talhada irrigated: RMSE = 0.62–1.77 mm d⁻¹ and d = 0.54–0.87), WP and WUE (Piracicaba irrigated = 1.08–1.85 mm kg⁻³ and 3.15–4.64 mm kg⁻³; Piracicaba rainfed = 0.79–1.85 mm kg⁻³ and 2.63–4.64 mm kg⁻³; Serra Talhada irrigated = 0.80–1.12 mm kg⁻³ and 2.37–3.09 mm kg⁻³). The ET_PT method with the R-2 approach showed better agreement with observed data in Piracicaba-SP, while the ET_PM method combined with S–R performed better for the data from Serra Talhada-PE.

Plants have evolved complex cell-type-specific processes to adapt to a dynamic environment, exhibiting distinct signals in response to emerging drought stress. We propose an advanced qualitative and quantitative analysis approach, demonstrating tissue specificity in drought adaptation, which in turn may provide novel biological insights. This study represents the first comparative immunolocalization of cell components in wheat roots and leaves subjected to graded drought stress. Leaf and root samples of wheat were collected at 0, 5, and 20 days under control and drought conditions, and analysed by confocal microscopy. We performed immunofluorescence labeling of specific cellular components in situ, and the acquired data were analysed in terms of changes in quantitative and spatial fluorescence intensity. The qualitative analysis revealed differences in terms of individual components and individual days of the experiment. The quantitative analysis of leaf anatomy showed that the most pronounced changes were observed in the level of proteoglycans (JIM13, JIM15) and polysaccharides (LM5, LM16, LM20). The leaves of plants growing in drought were characterised by severely deformed tissue regions, in which increased secretion of extensins, AGPs, galactans, hemicelluloses, and RG-I was noted. In turn, the qualitative analyses of the microscopy images of roots, along with fluorescence intensity analyses, revealed a significantly higher content of AGP and arabinoxylan in the exodermis in plants grown under drought stress. The amount of LM2-recognised AGPs in the root exodermis increased fourfold after 20 days of drought compared with well-watered controls. Our research has revealed that the changes at the tissue level are spatially localised and highly specific, highlighting the dynamic nature of cell adaptation in response to water stress. The obtained results also emphasise the importance of in planta analyses, which indicate that findings from only single ex planta studies may distort the entire image of changes occurring in the plant as a result of stress.

Extreme rainfall events have increased in recent decades because of global warming, and sesame (Sesamum indicum L.) is sensitive to waterlogging stress. The study aimed to (i) identify traits associated with waterlogging stress tolerance during the seedling and flowering stages of sesame and (ii) quantify the effects of foliar spray of 100 μM melatonin on increasing the seed yield of waterlogged stressed plants. Experiments were conducted to quantify the effects of waterlogging stress during the seedling and flowering stages using 35 genotypes. The third and fourth experiments were conducted to mitigate waterlogging stress by foliar spraying with 100 μM melatonin. Compared to the irrigated control, waterlogging stress during the seedling stage decreased the stay-green score (72%) and survival percentage of the seedlings (61.4%). Similarly, at the flowering stage, waterlogging stress decreased the pod-set percentage (68%), the number of seeds per capsule (29%) and seed yield per plant (16%). A strong correlation existed between the stay-green score and seedling survival percentage (r² = 0.75). Similarly, a strong correlation existed between pod-set percentage and seed yield plant⁻¹ (r² = 0.64). Among the genotypes, VRI 1 and NIC 8252 were identified as stress-tolerant, and Co-1 and SI-1771 were susceptible. Under waterlogging stress, foliar spray of melatonin during seedling and flowering stages increased the number of seedling survival m⁻² (65%) and pod-set percentage (66%), respectively, over the unsprayed control, resulting in an increased seed yield. Overall, sesame seed yield under waterlogging stress can be improved by developing lines with high pod-set percentage and/or by foliar application of 100 μM melatonin.

Sunflower (Helianthus annuus L.) is an important oilseed crop worldwide and threatened by salt stress due to climate change. The current study was devised to examine the impact of melatonin and salicylic acid (water soaked, 100 μM melatonin, 100 μM salicylic acid and 100 μM melatonin + salicylic acid) on growth and physiological traits of two sunflower hybrids, that is, AGUARA-4 and ORISUN-741 under three different salt levels (control, 75 and 150 mM NaCl). A pot experiment was carried out twice (two consecutive years) to check the combined effect of melatonin and salicylic acid on two sunflower hybrids. Melatonin and salicylic acid were applied as pre-sowing seed treatment on sunflower hybrid seeds. Results showed that electrolyte leakage (%) was increased in salt stress (150 mM NaCl) by 169.4% (AGUARA-4) and 194.8% (ORISUN-741) in both hybrids. In comparison with control, the combination of melatonin and salicylic acid improved the morphological attributes root and shoot fresh weight (45.2% and 41.9%) in AGUARA-4, whereas 81.5% and 46.1% in ORISUN-741 reduced the reactive oxygen species such as hydrogen peroxide and malondialdehyde content (35% and 27.2%) in AGUARA-4 and 33.8% and 25.1% in ORISUN-741 under salinity stress (150 mM NaCl). In conclusion, under salinity stress, ORISUN-74 showed a notable reduction in morphological attributes and enhancement in reactive oxygen species compared to AGUARA-4, whereas the combination of melatonin and salicylic acid improved the growth by decreasing reactive oxygen species and increasing the anti-oxidants. These results collectively suggested that the melatonin and salicylic acid, particularly their combination, were more effective in enhancing crop growth and production under saline conditions.

Cowpea (Vigna unguiculata L. Walp.) is a climate-resilient legume essential for food security and dryland farming in semi-arid regions of India. This study delineates climate-smart suitability zones by integrating Ensemble Species Distribution Modelling (ESDM) with Analytic Hierarchy Process–Multi-Criteria Evaluation (AHP–MCE) to incorporate climatic, soil, and land-use determinants. Seven modelling algorithms (GLM, GAM, MARS, CTA, RF, ANN, SVM) were applied under baseline, 2050, and 2070 climates (RCP 4.5 and 8.5), achieving strong ensemble performance (AUC = 0.84–0.88; TSS = 0.62–0.67). Precipitation variables dominated current suitability, while temperature extremes shaped future patterns. Integrating soil and LULC data through AHP–MCE substantially improved spatial precision, expanding optimum suitability zones and identifying new target regions in central, southern and eastern India. The integrated framework offers practical value for breeding programs (identifying heat- and drought-prone target environments), agricultural policy and investment (site-specific irrigation, diversification, climate-risk planning), and farm-level decision making. By quantitatively combining climatic, soil and land-use predictors in a unified GIS-based ensemble system, an advancement over existing legume suitability studies, this work provides a scalable tool for climate-resilient crop planning and adaptive agricultural development.

Global temperature rises and frequent heat waves increasingly threaten rice production by destabilising canopy and root-zone microclimates. Natural or conventional airflow management techniques often fail to provide precise, repeatable thermal regulation. In this study, we directly compared rotors airflow-engineered microclimates with ambient airflows across diurnal cycles (9:00 a.m., 12:00 p.m., 3:00 p.m.) and rice growth stages (heading, panicle, flowering). A rotor-based Wind Wall (WW) system was deployed to simulate rotors-induced airflow under controlled conditions, and computational fluid dynamics (CFD) validated rotor array performance, turbulence distribution, and wind field uniformity. Relative to ambient airflow, rotors airflow reduced canopy-top temperature variance by up to 48%, maintained peak midday temperature gradients at 0.11°C, and stabilised afternoon gradients to 0.06°C. Mid-canopy wind speed under rotors airflow decreased from 1.817 m s⁻¹ to 0.446 m s⁻¹ (−75.4%) at noon but rebounded to 0.843 m s⁻¹ (+89%) by 3:00 p.m., improving midday canopy stability. Turbulence intensity remained moderate (0.355–0.390), enhancing canopy aeration and gas exchange, while wind shear across plant layers stabilised between −0.12 and 3.91 s⁻¹. Physiologically, rotors airflow-engineered microclimates improved photosynthetic efficiency by 18%, reduced midday root-zone temperature variability by 33%, and decreased water loss by 14% compared with ambient conditions. Grain yield at flowering reached 43.2 g plant⁻¹, a 91% increase over restricted airflow and 23% higher than ambient airflow, with the harvest index rising to 37.24% (+14.8%). Across all growth stages and times of day, rotors-induced airflow consistently mitigated thermal stress, stabilised microclimate conditions, and enhanced nutrient uptake, resulting in more uniform grain filling and superior yield performance. These findings highlight UAV-based microclimate engineering as a precise and scalable strategy for controlling plant thermal and aerodynamic environments, offering a viable approach to climate change adaptation in rice production systems.

Crop-stage-specific deficit irrigation (DI) has been widely applied to achieve the optimum agricultural water use in dryland farming areas. However, the water use characteristics during different crop stages have not been fully investigated, considering ET uncertainties. It may hinder the correct decisions on optimum agricultural water management. This study investigated how the root-zone water budget components varied throughout the growing season in a summer maize field under three different irrigation regimes by using a soil water model, STEMMUS-ET, with the indirect and direct ET methods. Two successive years of crop-stage-specific DI experiments were conducted on a summer maize field in Northwest China to calibrate and evaluate the STEMMUS-ET model. Results indicate that STEMMUS-ET simulated the soil water contents, ET, soil evaporation, and root-zone water budgets well for all irrigation treatments. The influence of using different ET methods on soil moisture content mainly affects shallow soil layers (0–30 cm). The soil evaporation simulation was largely improved by the direct ET method due to the consideration of aerodynamic and surface resistance terms, especially after irrigation. Different irrigation amount has a significant effect on the transpiration but not on the soil evaporation. It is the frequency rather than the amount of irrigation that largely affects soil evaporation. Compared to CK treatment, the DI treatments depleted more soil water storage with less use of irrigation water throughout the growing season. T1, with the reduced irrigation water amount properly at the same irrigation frequency, can significantly improve WUE, increasing it by 9.71% compared to CK. These insights help make reasonable water management in dryland agriculture.

Rice (Oryza sativa L.) straw and roots are the primary sources of soil organic carbon (SOC) of paddies; however, variations in the quantity and quality of these residues under climate change remains unclear. This study investigated the changes in the rice residue biomass and the carbon-to-nitrogen ratio (C/N) under elevated [CO₂] (e[CO₂]) and air temperature (eT_air) for 2 years with naturally varying weather conditions. Rice was cultivated under different [CO₂]–T_air for 2019–2020, with longer sunshine hours and solar radiation (R_solar) during rice growing period in 2019 (675 h and 2079 MJ m⁻², respectively) than in 2020 (589 h and 1929 MJ m⁻², respectively). Rice biomass (grains, straw and roots), C gain and N uptake were measured, and C/N was determined. Compared to the ambient conditions, e[CO₂]–eT_air consistently increased straw and roots biomass for both years by 38.7% and 137.2% in 2019 and by 46.0% and 76.2% in 2020, respectively. However, under e[CO₂]–eT_air, C/N increased in 2019 (by 14.5%–31.6%), but decreased in 2020 (by 10.0%–12.2%) compared to ambient conditions. Comparing both years, straw and roots biomass were lower in 2020 than in 2019 by 19%–31% and by 31%–58%, respectively, with decreased C/N in 2020 by up to 32%. These results indicate that e[CO₂]–eT_air coupled with lower R_solar produces lower-quantity rice residues with high quality (i.e., a lower C/N) compared to those with higher R_solar, thus potentially reducing SOC accrual compared to higher R_solar conditions.