Search Results (142)

Accurately partitioning actual evapotranspiration ET_{c act} into soil evaporation E_s and plant transpiration T_{c act} is crucial for improving water use efficiency and devising precise irrigation schedules. In water-saving irrigated rice fields, ET_{c act}, E_s and T_{c act} were estimated using a dual crop coefficient method based on three approaches: FAO56 adjusted, locally calibrated and leaf area index LAI-based coefficients. Continuous measurements of hourly and daily ET_{c act}, E_s and T_{c act} with weighing lysimeters were used to validate these coefficients. Results showed that hourly ET_{c act}, E_s and T_{c act} exhibited a distinct inverted “U” shape single-peak trend. Daily ET_{c act} and T_{c act}, along with the corresponding crop coefficients K_{c act} and basal crop coefficients K_{cb act}, initially increased and then decreased throughout the rice growth stages, while daily E_s and soil evaporation coefficient K_{e act} were high during the initial stage and gradually decreased as the development stage progressed. FAO56 adjusted coefficients consistently underestimated both hourly and daily ET_{c act}, E_s and T_{c act}. Locally calibrated basal crop coefficients K_{cb Cal} were determined as 0.28, 1.17 and 1.09 for the initial, mid-season and end-season stages, respectively, and locally calibrated turbulent transport coefficient of water vapor K_{cp Cal} (recommended as 1.2 by FAO) was determined to be 1.59. Based on these calibrated coefficients, estimates of hourly and daily evapotranspiration ET_{c Cal}, soil evaporation E_{s Cal} and plant transpiration T_{c Cal} performed poorly during the initial stage but showed improved accuracy during subsequent growth stages. Hourly and daily evapotranspiration and its components based on LAI-based coefficients exhibited similar performance in estimating measurements, albeit slightly inferior to FAO56 calibrated coefficients. Overall, both the FAO56 calibrated coefficients and LAI-based coefficients are recommended for estimating evapotranspiration and its components at daily and hourly scales. These research findings provide valuable insights for optimizing irrigation regimes and improving water use efficiency in rice cultivation. Full article

Efficient water management and soil salinity are major constraints on cotton (Gossypium hirsutum L.) production in southern Xinjiang. This study evaluated the impacts of three irrigation leaching regimes (W1: 75 mm + 80% ETc, W2: 150 mm + 80% crop evapotranspiration (ETc), W3: 240 mm + 80% ETc) applied at different stages (seeding, budding, flowering), compared with a control of 450 mm spring irrigation (CK), on cotton growth, yield, quality, and water-use efficiency (WUE). The optimal leaching amount was found to range between 155–240 mm, with the W2C and W3C treatments performing the best. To integrate eight fiber indices, five growth parameters, yield, and WUE, comprehensive assessment models were established Four integrated evaluation models (Broda, Copeland, fuzzy Borda, and overall difference-based evaluation) exhibited strong consistency (Spearman coefficient > 0.98). Results from the fuzzy Borda model indicated optimal performance under treatments W2C and W3C. Additionally, a regression model suggested that cotton production was optimized when cumulative irrigation and rainfall reached approximately 326.3 mm, with leaching amounts applied during seeding, budding, and flowering stages. These findings provide practical guidelines for effective leaching practices to reduce soil salinity and to sustainably enhance cotton productivity in southern Xinjiang. Full article

Seasonal drought and nitrogen loss through runoff are two critical problems in the sloping land with shallow soils in southwest China. Irrigation is an effective way to alleviate drought and increase crop yields. Although irrigation is a proven strategy to mitigate drought stress and enhance yields, increased soil moisture under irrigation may exacerbate water and nitrogen losses. Therefore, this study aimed to investigate the long-term effects of irrigation regimes on crop yield, surface runoff, leaching, and nitrogen loss in shallow soil systems. Three experimental treatments were implemented: rainfed control (RF), single irrigation at a flowering stage (SI), and full irrigation (FI). The annual crop yield under SI and FI treatments was 16.4% and 43.5% higher than treatment RF, respectively. The surface runoff in RF was 46.2% and 52.8% higher than the values in SI and FI, respectively. Conversely, the leaching water volume in RF was 13.7% and 13.6% lower than in SI and FI, respectively. The total runoff did not differ significantly, as reduced surface runoff offset elevated leaching. The annual nitrogen loss was 35.4, 30.5, and 22.0 kg N ha⁻¹ in RF, SI, and FI treatments, respectively. Irrigation can significantly decrease the nitrogen loss. Leaching accounted for 96% of the total nitrogen loss. Enhanced crop nitrogen uptake under irrigation reduced total nitrogen concentrations in both soil and leaching water solution, which was the main factor for the decrease in total nitrogen loss under irrigation. These results indicate that in sloping land with shallow soil layers, optimal irrigation scheduling can effectively enhance crop yield without elevating nitrogen leaching risks. The study provides a scientific basis for formulating irrigation strategies in the study region. Full article

The increasing demand for renewable energy, coupled with the urgent challenges posed by climate change, has positioned perennial biomass crops (BPGs) as essential and sustainable alternatives for bioenergy production. This study investigated the impact of irrigation regimes on the physiological performance of three BPG species—Arundo donax L., Saccharum spontaneum, and Miscanthus—with a focus on leaf gas exchange (net assimilation rate and transpiration rate) and instantaneous water use efficiency (iWUE) at varying levels of irrigation input, adopting a split-plot experimental design under the Mediterranean climatic conditions of Sicily (Italy). The results clearly showed that A. donax, a C3 species, outperformed the C4 species S. spontaneum and Miscanthus, exhibiting significantly higher stomatal conductance and net photosynthesis, especially under irrigated conditions. S. spontaneum demonstrated the highest iWUE, particularly in rainfed treatments, reflecting its efficient use of water. Miscanthus showed the greatest sensitivity to water stress, with a more pronounced decline in photosynthesis during drought periods. This study accentuated the role of effective water management and genotype selection in optimizing biomass yield and resource efficiency, providing valuable insights for improving crop productivity in Mediterranean and other semi-arid regions. Full article

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R² = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m²) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development. Full article

In arid regions, water scarcity and soil potassium destruction are major constraints on the sustainable development of the jujube industry. In this regard, the use of crop models can compensate for time-consuming and costly field trials to screen for better irrigation regimes, but their predictive accuracy is often compromised by parameter uncertainty. To address this issue, we utilized data from a three-year (2022–2024) field trial (with irrigation at 50%, 75%, and 100% of evapotranspiration and potassium applications of 120, 180, and 240 kg/ha) to simulate the growth process of jujube trees in arid regions using the WOFOST model. In this study, parameter sensitivity analyses were conducted to determine that photosynthetic capacity maximization (A_max), the potassium nutrition index (K_status), the water stress factor (SWF), the water–potassium photosynthetic coefficient of synergy (α), and potassium partitioning weight coefficients (β_i) were the important parameters affecting the simulated growth process of the crop. Path analysis using segmented structural equations also showed that water stress factor (SWF) and potassium nutrition index (K_status) indirectly controlled yield by significantly affecting photosynthesis (path coefficients: 0.72 and 0.75, respectively). The ability of the crop model to simulate the growth process and yield of jujube trees was improved by the introduction of water and potassium parameters (R² = 0.94–0.96, NRMSE = 4.1–12.2%). The subsequent multi-objective optimization of yield and crop water productivity of dates under different combinations of water and potassium treatments under a bi-objective optimization model based on the NSGA-II algorithm showed that the optimal strategy was irrigation at 80% ET_c combined with 300 kg/ha of potassium application. This management model ensures yield and maximizes crop water use efficiency (CWP), thus providing a scientific and efficient irrigation and fertilization regime for jujube trees in arid zones. Full article

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

Water scarcity and spatial variability in soil fertility are key constraints to stable grain production in the Huang-Huai-Hai Plain. However, the interaction mechanisms between regulated deficit irrigation and soil fertility influencing yield formation and water-nitrogen use efficiency in winter wheat remain unclear. In this study, a two-year field experiment (2022–2024) was conducted to investigate the effects of two irrigation regimes—regulated deficit irrigation during the heading to grain filling stage (D) and full irrigation (W)—under four soil fertility levels: F1 (N: P: K = 201.84: 97.65: 199.05 kg ha⁻¹), F2 (278.52: 135: 275.4 kg ha⁻¹), F3 (348.15: 168.75: 344.25 kg ha⁻¹), and CK (no fertilization). The results show that aboveground dry matter accumulation, total nitrogen content, pre-anthesis dry matter and nitrogen translocation, and post-anthesis accumulation significantly increased with fertility level (p < 0.05). Regulated deficit irrigation promoted the contribution of post-anthesis dry matter to grain yield under the CK and F1 treatments, but suppressed it under the F2 and F3 treatments. However, it consistently enhanced the contribution of post-anthesis nitrogen to grain yield (p < 0.05) across all fertility levels. Higher fertility levels prolonged the grain filling duration by 18.04% but reduced the mean grain filling rate by 15.05%, whereas regulated deficit irrigation shortened the grain filling duration by 3.28% and increased the mean grain filling rate by 12.83% (p < 0.05). Grain yield significantly increased with improved fertility level (p < 0.05), reaching a maximum of 9361.98 kg·ha⁻¹ under the F3 treatment. Regulated deficit irrigation increased yield under the CK and F1 treatments but reduced it under the F2 and F3 treatments. Additionally, water use efficiency exhibited a parabolic response to fertility level and was significantly enhanced by regulated deficit irrigation. Nitrogen partial factor productivity (NPFP) declined with increasing fertility level (p < 0.05); Regulated deficit irrigation improved NPFP under the F1 treatment but reduced it under the F2 and F3 treatments. The highest NPFP (41.63 kg·kg⁻¹) was achieved under the DF1 treatment, which was 54.81% higher than that under the F3 treatment. TOPSIS analysis showed that regulated deficit irrigation combined with the F1 fertility level provided the optimal balance among yield, WUE, and NPFP. Therefore, implementing regulated deficit irrigation during the heading–grain filling stage under moderate fertility (F1) is recommended as the most effective strategy for achieving high yield and efficient resource utilization in winter wheat production in this region. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Selective water withdrawals (SWWs) are frequently used to minimize the downstream effects of dams by blending water from different depths to achieve a desired temperature regime in the river. In 2010, an SWW was installed on the outlet structure of the primary hydropower reservoir on the Deschutes River (Oregon, USA) to increase spring temperatures by releasing a combination of surface water and bottom waters from a dam that formerly only had a hypolimnetic outlet. The objective of increasing spring river temperatures was to recreate pre-dam river temperatures and optimize conditions for the spawning and rearing of anadromous fish. The operation of the SWW achieved the target temperature regime, but the release of surface water from a hypereutrophic impoundment resulted in a number of unintended consequences. These changes included significant increases in river pH and dissolved oxygen saturation. Inorganic nitrogen releases decreased in spring but increased in summer. The release of surface water from the reservoir increased levels of plankton in the river resulting in changes to the macroinvertebrates such as increases in filter feeders and a greater percentage of taxa tolerant to reduced water quality. No significant increase in anadromous fish was observed. The presence of large irrigation diversions upstream of the reservoir was not accounted for in the temperature analysis that led to the construction of the SWW. This complicating factor would have reduced flow in the river leading to increased river temperatures at the hydropower site during the measurement period used to develop representations of historical temperature. The analysis supports the use of numerical models to assist in forecast changes associated with SWWs, but the results from this project illustrate the need for greater consideration of complex responses of aquatic communities caused by structural modifications to dams. Full article

Investigating soil temperature and the heat transfer process is essential for understanding water–heat changes and energy balance in farmland. The conversion from upland fields (UFs) to paddy fields (PFs) alters the land cover, irrigation regimes, and soil properties, leading to differences in soil temperature, thermal properties, and heat fluxes. Our study aimed to quantify the effects of converting UFs to PFs on soil temperature and heat transfer processes, and to elucidate its underlying mechanisms. A long-term cultivated UF and a newly developed PF (converted from a UF in May 2015) were selected for this study. Soil water content (SWC) and temperature were monitored hourly over two years (June 2017 to June 2019) in five soil horizons (i.e., 10, 20, 40, 60, and 90 cm) at both fields. The mean soil temperature differences between the UF and PF at each depth on the annual scale varied from −0.1 to 0.4 °C, while they fluctuated more significantly on the seasonal (−0.9~1.8 °C), monthly (−1.5~2.5 °C), daily (−5.6~4.9 °C), and hourly (−7.3~11.3 °C) scales. The SWC in the PF was significantly higher than that in the UF, primarily due to differences in tillage practices, which resulted in a narrower range of soil temperature variation in the PF. Additionally, the SWC and soil physicochemical properties significantly altered the soil’s thermal properties. Compared with the UF, the volumetric heat capacity (C_s) at the depths of 10, 20, 40, 60, and 90 cm in the PF changed by 8.6%, 19.0%, 5.5%, −4.3%, and −2.9%, respectively. Meanwhile, the thermal conductivity (λ_θ) increased by 1.5%, 18.3%, 19.0%, 9.0%, and 25.6%, respectively. Moreover, after conversion from the UF to the PF, the heat transfer direction changed from downward to upward in the 10–20 cm soil layer, resulting in a 42.9% reduction in the annual average soil heat flux (G). Furthermore, the differences in G between the UF and PF were most significant in the summer (101.9%) and most minor in the winter (12.2%), respectively. The conversion of the UF to the PF increased the C_s and λ_θ, ultimately reducing the range of soil temperature variation and changing the direction of heat transfer, which led to more heat release from the soil. This study reveals the effects of farmland use type conversion on regional land surface energy balance, providing theoretical underpinnings for optimizing agricultural ecosystem management. Full article

Given the worsening global climate change that drives drought frequency and irrigation water shortages, implementing water-conserving practices like alternate wetting and drying (AWD) is now critically urgent. Biochar is widely used for soil carbon sequestration. However, there is limited information on the effects of biochar on soil organic carbon (SOC) and its labile fractions in paddy fields, especially under AWD. A two-year field experiment was conducted with two irrigation regimes (CF: continuous flooding irrigation; AWD) as the main plots and 0 (B₀) and 20 t ha⁻¹ (B₁) biochar as sub-plots. AWD had no effect on the SOC and particulate organic carbon (POC) content, but increased the dissolved organic carbon (DOC), microbial biomass carbon (MBC), easily oxidizable organic carbon (EOC), light fraction organic carbon (LFOC), and carbon pool management index (CPMI) at 0–10 cm depths, by 24.4–56.4%, 12.6–17.7%, 9.2–16.8%, 25.6–28.1%, and 11.3–18.6%, respectively. Biochar increased SOC while also increasing DOC, MBC, EOC, LFOC, POC, and CPMI at 0–20 cm depths, by 18.4–53.3%, 14.7–70.2%, 17.4–22.3%, 10.2–27.6%, 95.2–188.3%, 46.6–224%, and 5.6–27.2, respectively, making SOC more labile under AWD. Our results highlight that biochar still holds great potential for improving soil quality and carbon sequestration under AWD, and the combination of biochar and AWD can achieve the synergistic optimization of the food–water–carbon sequestration trade-off, which is beneficial to sustainable agricultural production. Full article

To identify optimal strategies for high-yield and high-efficiency cultivation under a “short-season direct-seeded cotton with once-only irrigation” regime, we conducted two-year field experiments (2022 and 2023) using a split-plot factorial design with three planting densities (30,000 (D1), 45,000 (D2), and 60,000 (D3) plants·ha⁻¹) and three nitrogen application rates (150 (N1), 180 (N2), and 210 (N3) kg·ha⁻¹). Our study systematically examined how these treatment combinations influenced canopy architecture, physiological traits, yield components, and fiber quality. The results showed that increased planting density significantly enhanced plant height, the leaf area index (LAI), and the number of fruiting branches, with the highest density (D3) contributing to a more compact and efficient canopy. Moderate nitrogen input (N2) significantly increased peroxidase (POD) activity, reduced malondialdehyde (MDA) accumulation, delayed functional leaf senescence, and prolonged the canopy’s photosynthetic performance. A significant interaction between planting density and nitrogen application was observed. The D3N2 treatment (high density with moderate nitrogen) consistently achieved the highest fruiting branch count, boll number per plant, and yields of both seed cotton and lint in both years, while maintaining stable fiber quality. This indicates its strong capacity to balance high yield with quality and maintain physiological resilience. By contrast, the D1N1 treatment (low density and low nitrogen) exhibited a loose canopy, premature photosynthetic decline, and the lowest yield. The D3N3 treatment (high density and high nitrogen) promoted vigorous early growth but reduced stress tolerance during later growth stages, leading to yield instability. These findings demonstrate that moderately increasing planting density while maintaining appropriate nitrogen levels can effectively optimize canopy structure, improve stress resilience, and enhance yield under short-season direct-seeded cotton systems with once-only irrigation. This provides both theoretical underpinning and practical guidance for achieving stable and efficient cotton production under such systems. Full article

Almond (Prunus dulcis Mill.) is characterized by its water stress tolerance and adaptability to diverse management strategies, allowing it to maintain or even enhance almond quality while achieving optimal yields. Limited research has been conducted to date on how almond production and quality vary across different water regimes and production systems, or how tree age modulates crop responses to deficit irrigation and organic practices. This study examines the effects of regulated deficit irrigation (RDI) under organic (OPS) and conventional (CPS) production systems, analyzing the impact on nut quality (physical and chemical parameters) and its sensorial properties in an almond orchard during seasons in 2019 and 2023, when the trees were 3-years old and when they were close to their yield potential at 7-years old, respectively. The PS and irrigation strategy affected the nut quality, yield, and tree growth. The OPS and RDI methods accumulated season-dependent yield losses in both studied periods. The kernel weight under OPS was lower than CPS in 2019, with these differences being less evident in 2023. The highest antioxidant activity and total phenolic compound values were obtained with the OPS and RDI methods in 2019, whereas the sugar and organic acid contents showed improvements under the OPS and the RDI strategy during 2019 and 2023, respectively. Finally, significant improvements were observed in relation to the fatty acids profile for nuts harvested under OPS in both seasons, especially in the latter season with RDI. Thus, almond quality can be enhanced by the integration of both OPSs and RDI strategies, although these improvements are dependent on tree age. Full article

Sorghum (Sorghum bicolor, (L.) Moench.), is one of the most important cereals in semi-arid and subtropical regions of Africa. However, in these regions, sorghum cultivation is often faced with several constraints. In Mali, terminal or post-flowering drought, caused by the early cessation of rains towards the end of the rainy season, is one of the most common constraints. Sorghum is generally adapted to harsh conditions. However, drought combined to heat reduce its yield and production in tropical and subtropical regions. To identify parents of sorghum hybrids tolerant to post-flowering drought for commercial hybrids development and deployment, a total of 200 genotypes, including male and female parents of the hybrids, were evaluated in 2022 by lysimeters under two water regimes, well-irrigated and water-stressed, at ICRISAT in Niger. Agronomic traits such as phenological stages, physiological traits including transpiration efficiency, and morphological traits such as green leaf number were recorded. Genotype × environment (G × E) interaction was significant for harvest index (HI), green leaf number (GLN), and transpiration efficiency (TE), indicating different responses of genotypes under varying water conditions. Transpiration efficiency (TE) was significantly and positively correlated with total biomass (BT), harvest index (HI), and grain weight (GW) under both stress conditions. Genotypes ICSV216094, ICSB293, ICSV1049, ICSV1460016, and ICSV216074 performed better under optimal and stress conditions. The Principal Component Analysis (PCA) results led to the identification of three groups of genotypes. The Groups 1 and 3 are characterized by their yield stability and better performance under stress and optimal conditions. These two groups could be used by breeding programs to develop high yield and drought tolerant hybrids. Full article