Search Results (51)

Selenium (Se) is a beneficial element that enhances plant antioxidant capacity, improves fruit quality, and contributes to the biofortification of horticultural crops. However, information regarding its effects on mineral nutrient balance and antioxidant metabolism in pomegranate remains limited. This study investigated the effects of foliar selenium (Se) application on fruit yield, mineral nutrient balance, antioxidant metabolism, and fruit quality of pomegranate (Punica granatum L.) cv. ‘Malas Saveh’ during the 2022 and 2023 growing seasons under orchard conditions in Iran. Trees were treated with sodium selenate at different concentrations using a randomized complete block design. In 2022, Se was applied at 0, 2, 4, and 6 mg L⁻¹, while in 2023, based on the results of the first-year screening phase, the concentration range was expanded to 0, 6, 8, and 10 mg L⁻¹ to further investigate plant responses to higher Se levels. Foliar Se application significantly increased fruit yield, fruit number, and Se accumulation in both leaves and fruits, confirming the effectiveness of Se biofortification. Selenium treatments also improved the nutritional composition of pomegranate fruits by increasing the concentrations of nitrogen (N), phosphorus (P), potassium (K), iron (Fe), and zinc (Zn), whereas manganese (Mn) concentrations declined, suggesting an antagonistic interaction between Se and Mn uptake. Significant improvements were observed in fruit quality traits, including soluble solids content, titratable acidity, vitamin C, total phenolics, anthocyanins, and antioxidant activity. The 6 mg L⁻¹ treatment in 2022 and the 8–10 mg L⁻¹ treatments in 2023 resulted in the most pronounced physiological and biochemical responses, with 10 mg L⁻¹ showing no further significant improvement for several key traits. Selenium application also enhanced the antioxidant defense system through increased activities of catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), phenylalanine ammonia-lyase (PAL), and ascorbate peroxidase (APX), while reducing hydrogen peroxide (H₂O₂), malondialdehyde (MDA), and membrane ion leakage. Principal component analysis further confirmed the strong positive association between higher Se concentrations and improved mineral and biochemical characteristics. Overall, foliar Se application effectively enhanced pomegranate productivity, nutritional quality, antioxidant capacity, and physiological performance, highlighting its potential as a sustainable agronomic practice for the production of high-quality Se-enriched fruits. Full article

Alfalfa (Medicago sativa L.) is widely recognized as a major forage crop, yet its role as a multifunctional biological input in sustainable horticultural production remains underexplored. This review evaluates alfalfa as a biological nitrogen source, organic fertilization resource, and biofertilizer-supporting crop within vegetable, medicinal, and perennial horticultural systems. Due to its high capacity for biological nitrogen fixation, alfalfa can supply substantial amounts of plant-available nitrogen, reducing dependency on synthetic fertilizers and supporting environmentally sound nutrient management. When used as green manure, cover crop, intercrop, mulch source, compost feedstock, or processed organic fertilizer, alfalfa enhances the soil organic carbon (SOC), improves soil structure, and increases the water-holding capacity properties particularly critical in intensive horticultural production. Higher SOC levels also contribute to the improved tolerance of horticultural crops to drought and heat stress through enhanced soil moisture retention and rhizosphere buffering. Alfalfa-based organic inputs stimulate rhizosphere microbial biomass, enzymatic activity, and functional genes associated with nitrogen cycling, strengthening plant–microbe interactions that underpin biofertilizer effectiveness. Evidence from vegetable and perennial systems indicates that alfalfa-derived amendments and rotations increase soil nitrogen availability, support yield stability, and improve soil health over the long-term. In orchards and vineyards, alfalfa cover cropping contributes to carbon sequestration, erosion control, and enhanced soil biological functioning. Overall, alfalfa emerges as a strategic species for integrating organic fertilization and biofertilizer-based approaches into modern horticultural systems, supporting reduced mineral fertilizer inputs while sustaining productivity, soil health, and environmental quality. Full article

Cultivated Land “Non-grain” Rectification is reshaping crop allocation across China, yet whether the policy promotes or impedes agricultural growth remains contested. This paper argues that the same uniform regulation generates spatially heterogeneous outcomes along a continuous topographic relief: strict enforcement on contiguous plain farmland raises compliance costs for horticultural production and displaces it toward higher-elevation counties, where land-use rules bind less tightly and micro-climates favor cash crops. Using a panel of 2077 Chinese counties from 2019 to 2023, we construct a municipal-level measure of rectification intensity from government work reports and examine how its effect varies with county-level terrain relief. The results show that the marginal effect of policy intensity on agricultural value added rises monotonically with terrain, turning from negative in flat plains to increasingly positive beyond 0.5–1.0 km of relief; at the sample mean a one-standard-deviation increase in policy intensity raises agricultural value added by about 0.36 percent, and at 2 km of relief by 1.16 percent. The mechanism is spatial reallocation, not land expansion. Rectification shrinks horticultural area in plains and expands it in mountains. A Moran’s I test confirms this: counties with very different terrain show opposite changes in orchard cover. Further heterogeneity tests indicate that rectification primarily promotes the relocation and expansion of fruit orchards toward higher-relief counties. The growth effect is stronger where transport networks are denser, whereas water endowment does not significantly moderate the effect. Results are robust to alternative keyword classifications, concurrent-policy controls, and two instrumental-variable strategies. Full article

Accurate hourly crop evapotranspiration (ETc) estimation is important for data-driven irrigation management support in tropical horticulture, yet existing approaches are constrained by data requirements and an inability to capture multi-scale temporal dynamics. This study proposes a Transformer encoder model for one-step-ahead hourly FAO-56 PM-derived ETc estimation in a durian orchard in Chanthaburi Province, Eastern Thailand, using 36,528 hourly meteorological observations obtained from the Visual Crossing Weather API for the orchard location over four years, with ETc computed from these inputs using the FAO-56 Penman–Monteith equation. The model employs a 168-h (7-day) look-back window, three stacked encoder blocks with multi-head self-attention ($h=8$, $d_{\mathrm{model}}=128$), and five meteorological input features (air temperature, relative humidity, solar radiation, wind speed, and ETc). A SARIMA$(2,1,2){(1,0,0)}_{24}$ model trained on the same dataset served as the statistical baseline. The Transformer achieved an RMSE of 0.0308 mm/h, MAE of 0.0188 mm/h, and $R^2$ of 0.9018 on the 168-h test set, outperforming SARIMA (RMSE = 0.0717, MAE = 0.0593, $R^2$ = 0.4688), representing a 57.0% reduction in RMSE, a 68.3% reduction in MAE, and a 92.4% improvement in $R^2$. The Transformer also achieved a daytime-only RMSE of 0.0414 mm/h vs. 0.0791 mm/h for SARIMA, and a daily cumulative ETc MAE of 0.1599 mm/day vs. 0.5901 mm/day, demonstrating superior accuracy during agronomically critical periods. The Transformer accurately reproduced both the 24-h diurnal cycle and the 7-day weekly pattern of ETc, whereas SARIMA exhibited a damped amplitude response. A recursive 168-h heuristic simulation demonstrated that the model generates physically plausible ETc patterns under an approximated meteorological scenario, suggesting the approach warrants further investigation as a component of future irrigation decision-support research. These results highlight the potential of Transformer-based deep learning for site-specific, proof-of-concept ETc estimation from meteorological inputs in tropical fruit production, pending validation across diverse sites and seasons. Full article

Water scarcity is a global phenomenon, and Pakistan is no exception to it. This study aims to assess the techno-economic efficiency of the irrigation system for guava orchard and melon crop in the Hafizabad District of Punjab province in Pakistan. The study has employed efficiency theory for a comparative analysis of modern and high-efficiency irrigation methods in contrast to old traditional methods of irrigation to estimate differentiating impacts on technical efficiency (TE), economic efficiency (EE), water productiveness, and crop yield. The mixed method approach is exercised on data collected from 108 stratified farmers (large, medium and smallholders) using structured surveys and qualitative insights. Beta-regression models using Cauchit link function are applied to translate determinants of TE/EE by taking into account predictor factors such as farming experience, operational costs and water productivity. Results show that solar irrigation systems have significantly better performance than the conventional system by having better TE and EE scores than conventional system performance. Farming experience and water productivity also have positive effects on efficiencies. Results also show that solar systems increase water productivity, lower costs and increase guava and melon productivity to a significant extent, which in turns aid in reducing the effects of salinity and evaporation in arid conditions. The overall finding supports and emphasizes solar’s supremacy for sustainable horticulture. Findings highlight the importance of incentivizing solar adaptation and agrivoltaic integration in Pakistan to ensure sustainable agriculture in water-stressed areas such as Punjab for food security and resource conservation for the production of guava and melons. Full article

Earthworms are ecosystem engineers that are sensitive to land-use intensification and edaphic conditions, yet their ecology remains poorly understood in transformed semi-arid landscapes. We hypothesized that, in recently colonized agroecosystems, land-use intensity and physicochemical soil conditions jointly filter the earthworm assembly. In the recently irrigated Lower Valley of the Negro River, Patagonia, Argentina, we sampled earthworms and soils across five land uses—riparian reference sites, fruit orchards, pastures, cereal crops, and horticulture plots—in landscapes dominated by Natrargid Ustolls and Fluventic Haplocambids. We found five species, all of which were exotic Lumbricidae, including the first Argentine record for Murchieona minuscula, indicating a recent colonization following human-mediated niche construction that created an ecological island. The earthworm abundance and biomass were highest in permanent and semi-permanent uses and were driven primarily by soil moisture, pH, and particulate organic matter. Crucially, our results reveal that land-use intensity filters communities by restricting the initial colonization rather than through local extinctions. These findings confirm that soil properties mediate the impact of land use on earthworm assemblages. The inclusion of pastures and fruit orchards in the rotations favors the earthworm populations that, despite low diversity, enhance soil functioning and contribute to agricultural sustainability in semi-arid irrigated agroecosystems. Full article

Agricultural plastic waste (APW) is an emerging source of soil pollution and potential micro- and nano-plastic (MNP) contamination in agroecosystems. This study focuses on the Apulia region in southern Italy, a key horticultural and viticultural area with intensive plastic use. Annual APW was estimated for each agricultural feature using a detailed 1:5000 land use map, crop distribution data, and validated plastic waste indices for several plastic application types. The analysis was integrated within a Geographic Information System (GIS) and combined with relative risk indices (RRIs) to compute and map the agricultural plastic pollution risk index (APPRI), a semi-quantitative indicator designated to estimate the potential release of MNPs from agricultural plastics. The APPRI is obtained by multiplying the APW estimates by the RRIs. The results show a clear spatial heterogeneity in plastic waste generation, with the highest APPRI values in vineyards, orchards, olive groves, and greenhouse systems, particularly in the provinces of Foggia and Bari. Cereal-based cropping systems exhibited the lowest risk values. The study proposes an innovative approach, combining land use, APW, and related potential risk into a single mapping tool. This allows for effectively identifying regional hotspots where management and recycling strategies should be prioritized. This GIS-based tool for assessing and visualizing agricultural plastic pollution risk can support evidence-based decision-making and sustainable waste management in agricultural landscapes. Full article

This study addressed the core challenge of intelligent pest and disease monitoring and early warning in smart horticultural production by proposing a multimodal deep learning framework based on multi-parameter environmental sensor arrays. The framework integrates visual information with electrical signals to overcome the inherent limitations of conventional single-modality approaches in terms of real-time capability, stability, and early detection performance. A long-term field experiment was conducted over 18 months in the Hetao Irrigation District of Bayannur, Inner Mongolia, using three representative horticultural crops—grape (Vitis vinifera), tomato (Solanum lycopersicum), and sweet pepper (Capsicum annuum)—to construct a multimodal dataset comprising illumination intensity, temperature, humidity, gas concentration, and high-resolution imagery, with a total of more than $2.6\times {10}^6$ recorded samples. The proposed framework consists of a lightweight convolution–Transformer hybrid encoder for electrical signal representation, a cross-modal feature alignment module, and an early-warning decision module, enabling dynamic spatiotemporal modeling and complementary feature fusion under complex field conditions. Experimental results demonstrated that the proposed model significantly outperformed both unimodal and traditional fusion methods, achieving an accuracy of $0.921$, a precision of $0.935$, a recall of $0.912$, an F1-score of $0.923$, and an area under curve (AUC) of $0.957$, confirming its superior recognition stability and early-warning capability. Ablation experiments further revealed that the electrical feature encoder, cross-modal alignment module, and early-warning module each played a critical role in enhancing performance. This research provides a low-cost, scalable, and energy-efficient solution for precise pest and disease management in intelligent horticulture, supporting efficient monitoring and predictive decision-making in greenhouses, orchards, and facility-based production systems. It offers a novel technological pathway and theoretical foundation for artificial-intelligence-driven sustainable horticultural production. Full article

Despite flies regularly visiting flowers, limited research has gone into their pollination ability on commercial crops. A national project in Australia aimed to identify fly species as potential managed pollinators for the horticultural industry and, in particular, avocado. This study investigated the ability of two calliphorids (Calliphora dubia and Calliphora vicina) and a syrphid (Eristalis tenax) fly species to pollinate Hass avocados in southwestern Australia. Four (4) field trials over three (3) years showed that each fly species (all found across Australia) was capable of pollinating Hass avocados when released into netted enclosures around multiple trees (12–26) during flowering. Trees enclosed with Eristalis tenax produced the highest fruit yield (18.0 kg/tree) outperforming trees pollinated by either C. dubia (11.6), managed honey bees in the open orchard (10.5) or C. vicina (6.8). Increasing fly numbers from 10,000 to 15,000 in the enclosures provided no additional pollination benefit. These results suggest that either E. tenax or C. dubia could be valuable managed pollinators for the avocado industry either with or without honey bees. Calliphora dubia was a significant pollinator during warmer flowering seasons and C. vicina was a useful pollinator during cold and wet flowering seasons. Full article

Geogenic contamination of groundwater presents a substantial threat to the enduring production and sustainability of irrigated fruit orchards, especially in arid and semi-arid regions where over 60% of horticultural irrigation depends on groundwater sources. Groundwater quality is increasingly threatened by geogenic contamination, presenting a critical global issue. Geogenic contaminants, such as fluoride and arsenic, combined with agricultural practices and inadequate wastewater treatment, pose a significant threat to groundwater. Concentrations of elements including arsenic, fluoride, boron, iron, and sodium often exceed acceptable thresholds. For instance, arsenic (As) levels up to 0.5 ppm have been reported in parts of South Asia, far exceeding the WHO guidelines limit of 0.01 mg/L. Boron concentrations above 2.0 ppm and fluoride concentrations exceeding 1.5 ppm are prevalent in impacted aquifers. Pollution consequences are far reaching, impacting agricultural ecosystems and human health as polluted water infiltrates the food chain via irrigation. These challenges are compounded by climate change and water scarcity, which further strain water sources, including those used in agriculture. Addressing groundwater contamination requires a multi-faceted approach. Strategies include developing crops that can tolerate toxicants, improving irrigation techniques, and employing advanced wastewater treatment technologies. This study solidifies current knowledge concerning the uptake processes and physiological effects of various pollutants in fruit crops. This review emphasizes the synergistic toxicity of many pollutants, identifies gaps in knowledge in species-specific tolerance, and emphasizes the dearth of comprehensive mitigating frameworks. Potential solutions, such as salt-tolerant rootstocks, gypsum amendments, and alternative irrigation timing, are examined to enhance resilient orchard systems in geogenically challenged areas. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

Kiwifruit, with its unique flavor, nutritional value, and economic benefits, has gained significant attention in agriculture production. Kiwifruit plants have traditionally been propagated without grafting, but recently, grafting has become a more common practice. A new and complex disease called Kiwifruit Vine Decline Syndrome (KVDS) has emerged in different kiwifruit-growing areas. The syndrome was first recognized in Italy, although similar symptoms had been observed in New Zealand during the 1990s before subsequently spreading worldwide. While kiwifruit was not initially grafted in commercial orchards, the expansion of cultivation into regions with heavy soils or other challenging environmental conditions may make grafting selected kiwifruit cultivars onto KVDS-resistant or -tolerant rootstocks essential for the future of this crop. Grafting is a common horticultural practice, widely used to propagate several commercially important fruit crops, including kiwifruits, apples, grapes, citrus, peaches, apricots, and vegetables. Grafting methods and genetic compatibility have a crucial impact on fruit quality, yield, environmental adaptability, and disease resistance. Achieving successful compatibility involves a series of steps. During grafting, some scion/rootstock combinations exhibit poor graft compatibility, preventing the formation of a successful graft union. Identifying symptoms of graft incompatibility can be challenging, as they are not always evident in the first year after grafting. The causes of graft incompatibility are still largely unknown, especially in the case of kiwifruit. This review aims to examine the mechanisms of graft compatibility and incompatibility across different fruit crops. This review’s goal is to identify potential markers and techniques that could enhance grafting success and boost the commercial production of kiwifruit. Full article

Mango (Mangifera Indica L.), a key horticultural crop, particularly in Pakistan, has been primarily studied locally using low- to medium-resolution satellite imagery, usually focusing on a particular phenological stage. The large canopy size, complex tree structure, and unique phenology of mango trees further accentuate intrinsic challenges posed by low-spatiotemporal-resolution data. The absence of mango-specific vegetation indices compounds the problem of accurate health classification and yield estimation at the tree level. To overcome these issues, this study utilizes high-resolution multi-spectral UAV imagery collected from two mango orchards in Multan, Pakistan, throughout the annual phenological cycle. It introduces MangiSpectra, an integrated two-staged framework based on Long Short-Term Memory (LSTM) networks. In the first stage, nine conventional and three mango-specific vegetation indices derived from UAV imagery were processed through fine-tuned LSTM networks to classify the health of individual mango trees. In the second stage, associated data such as the trees’ age, variety, canopy volume, height, and weather data were combined with predicted health classes for yield estimation through a decision tree algorithm. Three mango-specific indices, namely the Mango Tree Yellowness Index (MTYI), Weighted Yellowness Index (WYI), and Normalized Automatic Flowering Detection Index (NAFDI), were developed to measure the degree of canopy covered by flowers to enhance the robustness of the framework. In addition, a Cumulative Health Index (CHI) derived from imagery analysis after every flight is also proposed for proactive orchard management. MangiSpectra outperformed the comparative benchmarks of AdaBoost and Random Forest in health classification by achieving 93% accuracy and AUC scores of 0.85, 0.96, and 0.92 for the healthy, moderate and weak classes, respectively. Yield estimation accuracy was reasonable with $R^2=0.21$, and $\mathrm{RMSE}=50.18$. Results underscore MangiSpectra’s potential as a scalable precision agriculture tool for sustainable mango orchard management, which can be improved further by fine-tuning algorithms using ground-based spectrometry, IoT-based orchard monitoring systems, computer vision-based counting of fruit on control trees, and smartphone-based data collection and insight dissemination applications. Full article

Knowledge of nutrient surpluses in soils is critical to optimize nutrient management and minimize adverse environmental effects. We investigated the nutrient surpluses in soils in two regions over 25 years (1992 to 2017) in the south Loess Plateau, China. One region has cereals as the main crop, whereas in the other region, the main cereal crops was changed to kiwi orchards. The inputs of nitrogen (N), phosphorus (P), and potassium (K) increased rapidly (by 74%, 77%, and 103% from 1992 to 2017 in the cereal region; and by 91%, 204%, and 368% in the kiwifruit region), while the nutrient outputs were relatively stable, which resulted in increasing nutrient surpluses (the annual averaged surpluses of N, P, and K were 178, 62, and 12 kg ha⁻¹ y⁻¹ for the cereal region; and 486, 96, and 153 kg ha⁻¹ y⁻¹ for the kiwifruit region) and lower nutrient use efficiency (NUE). The higher N surplus in the orchard-dominated region caused high nitrate N accumulation (3071 kg N ha⁻¹ of 0–5 m in 11–20 y in the kiwifruit orchard) in deeper soil profiles. Similarly, high P and K surpluses in the orchard-dominated region increased soil available P and K. This highlights that comprehensive measures should be taken to control nutrient surpluses, which will help balance nutrient inputs and outputs and minimize nutrient losses in intensive horticultural crop systems. Full article

In the context of extreme climate change, experts in fruit production face a significant challenge in developing new strategies aimed at increasing the productivity of fruit tree crops. In order to investigate the changes in various horticultural indices (production, tree growth, and development) as well as the quality of plum fruits, sheep’s wool mulch, a cornstarch-based soil conditioner, and a combination of the two were applied in a Stanley plum orchard. In parallel, an experimental control variation was used. The results showed that the methods used had a substantial impact on fruit yield, size, and weight, with the best results obtained when mulching with sheep’s wool and soil conditioner. Plum fruits from mulching with sheep wool + soil conditioner exhibited the greatest total phenol concentration (1.30 ± 0.09 mg GAE/g dw), followed by the reference sample at 1.16 ± 0.09 mg GAE/g dw. The antioxidant capacity assessed using the three different methods provided favorable results for the experimental variant, sheep wool + soil conditioner. The results indicate that using the three experimental versions increased the fruit yield with 27% (sheep’s wool mulch) and with, 37% (sheep wool + soil conditioner) on average compared to that of the control group, while also improving the fruit quality. The fruit weight increased with 17.26% (cornstarch-based soil conditioner) and with 48.90% (sheep wool + soil conditioner) compared to that of the control, and the fruit size increased with 5% in two experiments (sheep’s wool mulch and a cornstarch-based soil conditioner) with 19% (sheep wool + soil conditioner), compared to the control group. Full article