Artificial intelligence (AI) is redefining hybrid breeding by shifting the operational paradigm from empirical field screening to predictive representation learning. While next-generation sequencing and high-throughput phenomics have generated massive multi-omics datasets, classical linear models fail to resolve the non-linear epistatic networks, dominance variances, and dynamic Genotype × Environment (G×E) interactions that govern heterosis and combining ability. This review provides a novel, critical synthesis of AI architectures in hybrid prediction, categorizing deep learning paradigms based on their biological inductive biases. We dissect the transition from single-modality models to intermediate cross-attention multimodal fusion networks that synthesize genomic markers, latent-space phenomics, and temporal environmental covariates. Furthermore, we address critical field bottlenecks—specifically the "Diallel Sparsity Paradox," population structure confounding, data privacy barriers, and the out-of-distribution generalization failures of models facing climate anomalies. To resolve these, we propose the integration of Explainable AI (XAI) for biological validation, Federated Learning frameworks for cross-institutional collaboration, and Physics-Informed Neural Networks (PINNs) that embed deterministic crop physiological equations into deep learning loss functions. Finally, we map out an operational roadmap for Executable Digital Twin (xDT) breeding ecosystems, establishing a definitive blueprint for next-generation, climate-resilient hybrid development.
Abiotic stresses such as drought, salinity, and temperature extremes, as well as their combinations, often occur during rice production, negatively affecting rice growth, yield, and quality. The impacts include reduced leaf area and photosynthesis, inhibition of spikelet formation and development, and oxidative stress. At the same time, tolerance is determined by the combination of germination capacity, root volume, root number, plant height, and tiller number. The latest research identifies genetic and epigenetic mechanisms of tolerance, including cold tolerance linked to DNA hypomethylation, which underlies acquired and heritable tolerance. The gene pool of wild, weedy, and pre-breed rice varieties can be used to improve cultivated rice’s yield potential and tolerance to multiple stresses. Studies on rice’s response to salt stress reveal the role of QTL Saltol and mechanisms of ion homeostasis and antioxidant defense that can be harnessed to improve tolerance. The authors conclude that combined physiological, genetic, molecular, epigenetic, and multi-omics approaches, along with the utilization of the wild rice gene pool, speed breeding technologies, genome editing, transgenics, marker-assisted selection, QTL mapping, and GWAS, could be used to develop novel varieties with increased tolerance to multiple stresses, stable yield, and improved grain quality.
Arsenic and fluoride are widespread geogenic contaminants of freshwater systems in South and Southeast Asia, where they frequently co-occur in groundwater-fed aquaculture and capture-fishery habitats. Although their individual toxicity to fish is documented, combined arsenic–fluoride effects on hematological and oxidative stress endpoints remain uncharacterized for the barred spiny eel, Mastacembelus puncalus (Hamilton, 1822; accepted name Macrognathus pancalus), an economically important food fish. The 96-h median lethal concentrations (LC50), determined by probit analysis, were 28.0 mg/L for sodium arsenite and 150.0 mg/L for sodium fluoride (NaF). Fish (n = 10 per group) were subsequently exposed for 30 days to six treatments: control; arsenic at 2.8 and 5.6 mg/L; fluoride at 15 and 30 mg/L; and combined arsenic (2.8 mg/L) + fluoride (15 mg/L). Hematological indices (red and white blood cell counts, hemoglobin, packed cell volume, erythrocytic indices, and platelets) and oxidative stress biomarkers (lipid peroxidation [LPO], superoxide dismutase [SOD], and catalase [CAT] in gill and liver) were compared by one-way analysis of variance with Tukey’s honestly significant difference test. Exposure induced dose-dependent anemia, most severe in the combined treatment, in which red blood cell count, hemoglobin, packed cell volume, and platelets declined by 38.0%, 41.7%, 41.1%, and 37.9%, respectively, whereas white blood cell counts increased by 82.1%. Gill LPO rose by 240.6% and liver LPO by 202.2% relative to controls, while SOD and CAT activities were suppressed by approximately 58–60% in both tissues, with higher absolute baseline activities in liver; all responses intensified progressively over 30 days. These findings constitute the first combined arsenic–fluoride toxicity assessment in this species and support hematological indices and oxidative stress biomarkers as early-warning tools for biomonitoring and food safety in contaminated aquatic systems.
The research was commenced to sought and evaluate the fixation and recovery rate dynamics of phosphorus and potassium across distinct land uses, shedding light on the relationship between these patterns. Soil samples were gathered at a depth of 0-15 cm from three land-use categories viz., Forest, Agriculture, and Horticulture, within the Ganderbal district of the temperate Himalayas. A total of 75 soil samples were collected from various locations for analysis. The results from different analysis revealed that the soils were slightly acidic to neutral in reaction and soil texture varied from silty clay loam to silt loam. The highest mean value of bulk density (1.44 g cm-3) was recorded in grapes and rice soils, highest mean value of particle density (2.54 g cm-3) was recorded in grapes soils and lowest (2.33 g cm-3) in forest soils. Porosity was observed highest (44.14%) in forest soil. No salinity hazards were observed as electrical conductivity values among studied land uses were less than 1 dSm-1. The soil organic carbon was found highest (1.66 %) in forest soils and lowest (1.06 %) in apple soils. The cation exchange capacity was observed highest mean value (15.47 Cmol (+) kg-1) in rice soils and lowest (14.27 Cmol (+) kg-1) in forest soils. The findings revealed noteworthy differences in phosphorus and potassium fixation and recovery rates among the land-use types. Phosphorus fixation rate was found to be highest in forest areas, reaching 66%, while the lowest fixation rate was observed in Apple orchards at 38%. Intriguingly, the recovery rate of phosphorus exhibited an inverse pattern, with the highest recovery observed in Apple orchards at 62%, and the lowest in Forest areas at 34%. Similarly, for potassium, Forest and orchards exhibited the highest fixation rate at 58%, contrasting with Maize, which had the lowest fixation rate at 46%. The recovery rate of potassium demonstrated a similar trend, with the highest recovery rate occurring in Maize at 62%, and the lowest in Forest orchards at 42%. These findings underscore the intricate interplay between land use and nutrient dynamics, providing valuable insights for sustainable land management practices in the Ganderbal district.
The Shekhawati region of north-eastern Rajasthan is a dryland ecosystem. It has high temperatures, uneven rainfall and substantial climatic fluctuations create challenging environmental conditions for natural vegetation and biodiversity. This study assessed long-term changes in key climatic variables, including minimum, mean and maximum temperature, annual rainfall, relative humidity and wind speed, across Churu, Sikar and Jhunjhunu districts over the period 1981–2025. Temporal patterns were examined using decadal averages, descriptive statistical measures and linear regression analysis to identify the direction and magnitude of climatic change. The analysis revealed an overall warming pattern across the three districts, although the magnitude of change varied spatially. Churu generally experienced higher temperatures, with a long-term annual mean temperature of 26.77 °C, followed by Jhunjhunu (26.18 °C) and Sikar (26.05 °C). Rainfall remained highly variable from year to year; nevertheless, a positive long-term trend was observed in all three districts. The estimated annual rates of increase were 3.714 mm year⁻¹ for Churu, 5.028 mm year⁻¹ for Sikar and 4.533 mm year⁻¹ for Jhunjhunu, and the regression analyses indicated significant temporal relationships. Relative humidity also showed a clear upward tendency, particularly in Churu, where the regression model explained 54.7% of the observed variation (R² = 0.547). The annual wind speed was observed in range from 2.0–2.5 m s⁻¹ in Churu, 2.1–2.5 m s⁻¹ in Sikar, and 1.9–2.4 m s⁻¹ in Jhunjhunu. The district-level climatic trends provide an important baseline for evaluating climate-related changes in biodiversity and for developing appropriate conservation and ecosystem-management strategies in the semi-arid landscapes of the Shekhawati region.
An orthogonal experiment (three factors × three levels) was designed to establish a cutting propagation technique for Hevea brasiliensis rootstock seedlings. The effects of substrate, exogenous hormone type, and concentration on rooting were evaluated across nine treatments. Rooting rate, four root traits, and mineral element contents were measured, and rooting performance was comprehensively assessed using the membership function method. Substrate was found to be the dominant factor, significantly affecting rooting rate, total root length, primary root number, and K, Ca, Mg, Fe, and Zn contents. Neither hormone type (IAA, IBA, NAA) nor concentration (100–500 mg/L) exhibited significant effects on rooting. The soil yielded the highest mean membership value, with the soil + 500 mg/L IAA treatment ranking first (0.984). Overall, the highest rooting rate and root traits were obtained with soil and 500 mg/L IAA, which is recommended as the optimal protocol for rooting of rubber tree seedling cuttings.
A study was conducted in Kalyan Karnataka region of Karanataka state during the year 2023-24 to identify and document the underutilized vegetables of the study area. The exploratory research design was employed in conduct of the study. Primary data were collected by using the questionnaire prepared for the study, a snow ball method of data collection were used to collect data due to lack of underutilized vegetables collecting and marketing farmers list. The suitable statistical tools like frequency and percentage are used for the analysis and tabulation of the data. In this study ten underutilized vegetables were identified, their common name, scientific name, family, health benefits and their images were documented. The identified vegetables are pigweed, joyweed, basale, nelabasale, brahmi, pointed gourd, moringa leaves, little wild gourd, safflower leaves and chickepea leaves. The results of the study revealed that majority of the farmers sold these underutilized vegetables directly to the consumer and low percent of middle men involved in the marketing. With respect to consumption pattern majority consumed these vegetables seasonally followed by rarely and never. Lack of knowledge regarding health benefits (92.86%) and non commercial production system (90.00%) are the major constraints for the underutilizing these vegetables. Vast potential of underutilized vegetables contributing significantly to the livelihood of the rural population, enhancing food and nutritional security and fostering sustainable agricultural development, hence government has to initiate the promotional activities for these underutilized fruit crops and provide the platform to sell their vegetables at suitable price. Underutilized vegetables in the Kalyana Karnataka region offer a unique opportunity to promote nutritional diversity, climate resilience, and local food traditions. With proper support through policy, awareness, and market linkages, these neglected crops can become mainstream contributors to sustainable agriculture and rural development.
To improve the ecological condition of the mining area, the Maoming Open-pit Mine in Guangdong Province launched a systematic ecological restoration project in 2013 to gradually restore the mining ecosystem. To quantitatively evaluate the effectiveness of the restoration, this study used Landsat series remote sensing images from 2013 to 2021 and applied the Pixel Dichotomy Model to estimate Fractional Vegetation Cover (FVC) as the primary evaluation indicator. The spatiotemporal dynamics of vegetation cover before and after ecological restoration were analyzed to quantitatively assess restoration effectiveness. The results show that: (1) The proportion of areas with low vegetation cover continuously decreased from 94.91% to 60.53%, whereas the proportion of areas with medium-high and extremely high vegetation cover increased from 0.11% to 29.16%. Vegetation recovery gradually spread from the periphery toward the interior of the pit. (2) The distribution of vegetation cover classes improved significantly, with a substantial reduction in bare soil areas and a marked increase in areas with medium and high vegetation cover, indicating a significant improvement in overall vegetation coverage. (3) Over the study period, the average vegetation cover in the mining area increased steadily from 1.18% to 18.00%, representing a substantial improvement in vegetation conditions. In conclusion, the restoration practices and outcomes of the Maoming Open-pit Mine provide valuable insights for the ecological restoration and post-restoration effectiveness assessment of similar legacy open-pit mines across China.
Since conservation conflicts occur within a particular cultural, political, and social context, they must be analyzed and addressed within the same context. This paper examines the pastoral- conservation conflicts in the context of Tanzania’s national parks using the case study of Saadani National Park (SANAPA), with the view to understanding their nature, root causes, and sustainable solutions to such conflicts. Fieldwork involved multiple methods of data collection: in-depth interviews with conservation management officials, community leaders, pastoralists, and an NGO dealing with pastoral issues; focus group discussions with small-scale farmers (peasants); informal discussions with the wider community; document analysis; and field observations. These were coupled with a four-month period stay in the study area plus the researcher’s experience with the wider community. The results indicate that the main conflict between conservation and pastoralism in the study area is the encroachment into SANAPA by livestock in search of pasture and water. The livestock encroachment, however, is seasonal and is done by migrating pastoralists who are not originally from around the park. The conflict happening in this particular case study is more than a resource - use conflict driven by access denial. It is also more than being prompted by changing climate patterns that have caused sustained drought in pastoral areas. The conflict is prompted by a mobile form of livestock keeping and herd sizes embedded in pastoral culture and value systems, and is reinforced by the practices of soliciting bribes embraced by conservation management staff, politicians, administrators, police and magistrates. With these practices, conflicts between pastoralism and other forms of livelihoods (not necessarily conservation) in Tanzania are likely to continue for unforeseeable future.
The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is a representative region in China characterized by a high level of urbanization, rapid land-use changes, and intensive population and economic activities. Identifying the spatiotemporal patterns and driving factors of daytime and nighttime surface urban heat island (SUHI) intensity is of great significance for regional thermal environment management and territorial spatial planning. Based on multi-source datasets from 2018 to 2024, this study developed an analytical framework consisting of “SUHI pattern identification–land-use transition response–driving mechanism interpretation” to reveal the evolution and driving mechanisms of the thermal environment in the GBA from a diurnal perspective. The results showed that: (1) High SUHI values during both daytime and nighttime were concentrated in core built-up areas such as Guangzhou, Foshan, Dongguan, and Shenzhen, whereas low-value areas were mainly distributed in mountainous forest regions in the north and coastal water bodies along the Pearl River Estuary. (2) From 2018 to 2024, SUHI intensity exhibited overall fluctuations, with an increasing trend in the eastern urban corridor of the Pearl River Delta, whereas the northwestern mountainous areas remained relatively stable or experienced a slight decline. (3) The conversion of forestland, cropland, and water bodies/wetlands to construction land significantly intensified SUHI, with the strongest warming effect observed when water bodies/wetlands were converted to construction land. Conversely, the transformation of construction land into green spaces or water bodies effectively mitigated SUHI. (4) XGBoost-SHAP analysis revealed that NDBSI, NDVI, NDMI, MNDWI, and NTL were the key factors influencing daytime SUHI, whereas NDBSI, NTL, PD (population density), GDP, and NDVI were the primary drivers of nighttime SUHI. These findings provide a scientific basis for thermal environment regulation, urban expansion control, and blue-green space conservation in the GBA.
As countries accelerate their transition towards low-carbon economies, climate finance taxonomies have emerged as a critical policy instrument for channeling financial flows into environmentally sustainable activities. A climate finance taxonomy is defined as a structured framework to classify economic activities based on their alignment with climate objectives such as enhancing resilience and supporting transitions in carbon- intensive sectors. They provide common definitions and benchmarks to reduce greenwashing and enhance transparency (Network for Greening the Financial System, 2022). Most of the existing taxonomies, like UK, EU have been developed in advanced economies with market-oriented financial systems, hence raising questions about their relevance for emerging economies such as India. In India, banks dominate the financial system and play a pivotal role in financing infrastructure, industry and micro, small and medium enterprises (MSMEs) that are vital for growth yet highly carbon-intensive. India's Draft Climate Finance Taxonomy, released by the Ministry of Finance (MoF), in May 2025, seeks to address this challenge by guiding climate-aligned finance while clearly recognizing national development priorities and transition realities. The practical implications of the taxonomy for Indian banks are still underexplored. This study reviews a comparative literature of global climate finance taxonomies and finds their applicability to the Indian banking sector. Using a qualitative and comparative methodology, the study reviews international climate taxonomies’ frameworks alongside India’s draft taxonomy. The findings suggest that although global taxonomies provide valuable reference points for standardization and climate risk management, their direct transplantation into India’s banking system may be inappropriate. India’s Draft Taxonomy 2025, with its emphasis on transition pathways and development sensitivity, appears better aligned with domestic realities. This study has explored the literature on comparative evolution of climate finance taxonomies and their implications for India’s bank‑dominated financial system. The paper concludes with policy-relevant recommendations for regulators and banks to strengthen taxonomy implementation while advancing sustainable and inclusive economic growth.
Recent advances in high-throughput genotyping, phenotyping, and multi-omics technologies have generated large, heterogeneous datasets spanning genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics across plant species. Conventional statistical and machine-learning approaches often struggle to integrate these modalities, limiting mechanistic insight and predictive accuracy for complex genotype-environment-phenotype relationships. Artificial intelligence (AI), particularly deep learning and graph-based architectures, provides powerful tools for multi-omics data fusion, regulatory network reconstruction, and prediction of polygenic, environmentally modulated traits. This review synthesizes current applications of AI-driven multi-omics integration in plant science, with a focus on gene function annotation, cellular network inference, and complex crop phenotype prediction. We examine key methodological frameworks including autoencoders, variational generative models, multimodal transformers, graph neural networks, and self-supervised foundation models, highlighting representative case studies in Arabidopsis and major crops such as rice, maize, wheat, and tomato. We critically assess challenges related to data quality, batch effects, domain shift, metadata standardization, model interpretability, and reproducibility, and outline future directions encompassing plant-specific foundation models, pan-omics integration, digital-twin cropping systems, and community-driven benchmarking.
Conducting long-term spatiotemporal monitoring of ecological environment quality and identifying its driving mechanisms are of great significance for coordinating ecological protection with high-quality regional economic development. Based on Landsat remote sensing data from 2005 to 2025 on the Google Earth Engine (GEE) platform, this study employed Principal Component Analysis (PCA) to construct the Remote Sensing Ecological Index (RSEI), analyzing the spatiotemporal evolution patterns of RSEI in Yantai City. The study further utilized the explainable extreme gradient boosting model (XGBoost-SHAP) to reveal key natural and anthropogenic drivers of ecological environment quality, along with their nonlinear and interactive effects. The results indicate that: (1) On a temporal scale, the RSEI of Yantai City exhibited a fluctuating trend of initial decline followed by an increase, then another decline and subsequent rise, with a multi-year average of 0.438, indicating an overall moderate level of ecological environment quality; (2) Spatially, the RSEI in the study area showed significant heterogeneity, with high-value areas concentrated in central Yantai and low-value areas distributed near the coast; (3) Ecological quality was primarily driven by land use type and precipitation, with anthropogenic factors also playing a significant role. This research provides methodological references and scientific foundations for ecological monitoring, risk assessment, and territorial spatial optimization in coastal cities.
This study evaluated the effects of soil properties from five locations in Benue State, Nigeria, on the early growth of Tectona grandis (teak) seedlings. Bulk composite soil samples (0–30 cm depth) were collected 500 m from teak plantations in Amilogodo (Oju), Aghan (Makurdi), Mbagba (Ushongo), Ihugh (Vandekiya), and Ijami (Ohimini). Soils were analyzed for physico-chemical properties, and treated seeds were germinated then transplanted into polypots for six months. Growth parameters included seedling height (cm), leaf number, leaf area index (LAI; cm²), and dry biomass (g). Data were analyzed using one-way ANOVA and LSD tests (p< 0.05). Soil E (Ohimini; pH 6.10, CEC 7.52 cmol kg−1, organic C 2.28%, total N 0.09%, available P 6.75 mg kg-1) yielded superior performance (height 11.74 cm, LAI 11.30 cm², dry biomass 4.44 g). Well drained loamy soil with high nitrogen content, high CEC, organic matter content and available phosphorus is most suitable for the establishment of Tectona grandis plantation. Therefore, soils with characteristics similar to that of sample E (Ohimini) is recommended for the establishment of Teak (Tectona grandis) plantation Benue State.
Traditional, lecture-based pedagogical models often fail to translate environmental knowledge into real-world sustainable habits, creating a persistent "knowledge-action gap" among young learners. While students may memorize theoretical facts, they frequently lack the practical social consciousness required to drive meaningful green campus transformations. This study evaluated the efficacy of an activity-led instructional intervention in enhancing environmental responsibility and sustainability literacy among secondary school students. The research focused on critical dimensions that are sustainable lifestyles and eco-conscious practices, energy usage patterns and their broader environmental footprints and resource conservation and energy efficiency. A quasi-experimental research design was employed with 11th 70 grade students from a CBSE-Board school in Lucknow city. The experimental group (N=35) received a 45-day structured instructional program using cooperative learning strategies, while the control group (N=35) followed the traditional teaching learning. Data were analysed using non-parametric Wilcoxon Signed Ranks and Mann-Whitney U tests via SPSS to measure internal growth and comparative significance. The findings revealed that the activity-led program caused a highly significant improvement in the experimental group across all dimensions. Notably, the energy usage dimension showed a near-universal internal improvement rate. Comparative analysis proved the experimental group achieved significantly higher mean ranks than the control group in resource conservation and energy efficiency, demonstrating the superior impact of structured interventions. The study demonstrates that specialized, activity-based interventions are superior to conventional methods in fostering ecological stewardship. It recommends integrating hands-on modules into mainstream curricula to bridge the gap between environmental awareness and active conservation.
Landscape elements within tertiary institutions play a significant role in enhancing users’ physical, psychological, and social well-being by providing spaces for relaxation, interaction, and environmental comfort. In rapidly urbanizing environments such as Lagos State, public tertiary institutions often experience high population density and infrastructural pressure, making the quality and effectiveness of outdoor landscapes critical to users’ daily experiences. However, many institutional landscapes face challenges such as poor maintenance, inadequate design, limited vegetation, and insufficient seating or shading, which may reduce their potential benefits. While previous studies have examined green spaces in urban contexts, limited research has focused on how specific landscape elements within tertiary institutions influence users’ well-being. This study therefore assesses the effectiveness of landscape elements on users’ well-being in selected public tertiary institutions in Lagos State. The objectives are to identify key landscape elements, examine users’ perceptions, and evaluate their impact on physical, psychological, and social well-being. A quantitative research approach will be adopted using structured questionnaires and site observations. Data will be analyzed using descriptive and inferential statistics. The study aims to provide evidence-based recommendations for improving landscape design and management in tertiary institutions to enhance user well-being and overall campus experience.
This study aimed to quantify flavonoids and polyphenols among 10 different rice varieties, from white to brown rice, and to describe the gene expression profiles related to biosynthesis.The study analyzed the phenolic content (TPC), flavonoid content (TFC), and total anthocyanin content (TAC) of 10 rice varieties in HATRI . Results showed:The TPC of brown rice and white rice of different varieties differed significantly (p < 0.05). The measured TPC in brown rice was significantly higher (118.98-206.06%) than in white rice. The highest TPC (Total Flavouring Content) of brown rice was found in Black Rice (771,15 mg/100 g).The TFC of brown rice and white rice from different varieties differed significantly. The TFC of brown rice ranged from 178.74–526.65mg/100 g, while white rice ranged from 85.41–432.15 mg/100 g: brown rice had a 10%–20% higher total flavonoid content than white rice. Among these, the difference between brown rice and white rice from the OM5451 and HATRI 722 varieties was smaller. The content of free and binding anthocyanins in the rice fraction of ten different genotypes of rice with brown rice and white rice ranged from 15.32 to 183.05 and 6.18 to24.16 mg of Cy3-GE/100 g DM, respectively. The highest anthocyanin concentrations were found in free form. Black rice (HATRI 40) showed the highest anthocyanin content (234.62 mg Cy3-GE/100 g DM), followed by HATRI 11 variety (73.88 mg Cy3-GE/100 g DM) and white rice (50.42 mg Cy3-GE/100 g DM). Our studies have enriched the active compounds of rice and laid a solid foundation for improving the active compounds for functional food rice in Vietnam.
This study examines the influence of salinity on dissolved oxygen (DO) in pond ecosystems and evaluates its consequences for pisciculture. Dissolved oxygen is one of the most important indicators of water quality because it directly affects fish metabolism, growth, reproduction, immunity, and survival. Salinity alters the physical and chemical properties of water and consequently affects oxygen solubility. Sample observations demonstrated a progressive decline in dissolved oxygen concentration from 8.5 mg/L to 5.9 mg/L as salinity increased from 0 to 12 ppt. The results suggest a strong negative relationship between salinity and dissolved oxygen. Such reductions can adversely affect pond productivity and fish yield. Effective monitoring and management of salinity and oxygen levels are therefore essential for sustainable aquaculture.
To clarify the regulatory effects of irrigation frequency and leaf whorl position on axillary bud quality of rubber tree ‘Reken 628’, a two-factor split-plot experiment was conducted using 3-year-old single-stem bud sticks. Two irrigation frequencies (once daily and twice daily) and three leaf whorl positions (2nd, 3rd, 4th reverse leaf whorls) were set. Morphological and physiological indices were measured, followed by ANOVA, correlation analysis, TOPSIS and PCA. The results showed that irrigation frequency had significant effects on leaf water content and scale bud eye length, with a significant interaction between irrigation frequency and leaf whorl position. Leaf width was extremely significantly positively correlated with leaf water content, and leaf water content was extremely significantly negatively correlated with scale bud scar thickness. TOPSIS evaluation showed that the highest axillary bud quality was observed under twice daily irrigation + 2nd reverse leaf whorl, with a comprehensive index of 0.7785. Under the same leaf whorl, twice daily irrigation was significantly better than once daily irrigation. This study provides a technical basis for standardized cultivation of ‘Reken 628’ bud sticks.
As significant actors in the agricultural sector, women farmers have become increasingly active in adopting various strategies to mitigate the impacts of climate change in Dodoma. However, Dodoma is a semi-arid region; thus, its climate change adaptation strategies differ from those of other regions of Tanzania. The study focuses on the strategies they adopt to cope with climate change impacts in Dodoma. Using the content analysis of publicly available documents was conducted to evaluate the experiences, knowledge, and farming practices of women farmers, thereby helping identify factors that influence their adaptation choices. The study finds that access to resources, technologies, and extension services shaped the variation of women farmers’ adaptation strategies. Women farmers use various adaptation strategies, such as crop rotation, genetically modified seeds, improved water management, soil health practices, diversification, and intercropping, to mitigate the impacts of climate change. The study findings have far-reaching implications. First, local governments in Dodoma need to implement targeted interventions to encourage widespread adoption among women farmers, thereby fostering agricultural sustainability and resilience. Second, financial institutions should introduce incentives to promote soft loans, enabling women farmers to access advanced agricultural inputs and invest in commercial farming.
