High Crop Research Articles

Changes in phosphorus-related performance attributes of dryland winter wheat cultivars released between the 1940s and 2010s in Shaanxi Province, China.

Water and nutrients are two main determinants of wheat yield, which are vital for maintaining high crop yields. In the present study, the effects of water and phosphate fertilization on wheat yield, photosynthetic parameters, water productivity and phosphate use efficiency were investigated. Five dryland wheat cultivars from the 1940s to the 2010s that are widely cultivated in Shaanxi Province, China, were used. Experiments were conducted from 2019 to 2022 using two irrigation levels (normal rainfall and no precipitation after the reviving stage) and two phosphorus application levels (0 and 100 kg ha-1). Compared with old cultivars ('Mazha'), the grain yield of modern cultivars ('Changhan 58') was 89.24% higher and was closely correlated with chlorophyll index, leaf area index, photosynthetic rate and tillers. With the replacement of cultivars, the phosphorus content, water potential and phosphatase activity of wheat leaves increased. Considering water-phosphorus interactions, the water use efficiency and phosphorus use efficiency of wheat showed a significant positive correlation. Our findings indicate that modern wheat cultivars are more responsive to phosphorus. Further analysis revealed that modern varieties have evolved two phosphorus absorption strategies in response to phosphorus deficiency - namely, the formation of a phosphorus supply source, which may result in larger numbers of green organs; and an increase in phosphorus sinks, which tended to activation and transport of plant phosphorus. Our results may thus contribute to water conservation, increased yields and the development of strategies for efficient phosphorus fertilization. © 2024 Society of Chemical Industry.

Design and testing of an intelligent variable-rate spraying system based on LiDAR-guided application

Traditional plant protection machinery cannot apply pesticides according to the actual growth demand of crops, which causes a lot of pesticide waste and serious environmental pollution. Based on this, an intelligent variable-rate application system based on 3D LiDAR (light detection and ranging) scanning and PWM (pulse-width modulation) control for maize canopy was developed in this paper. After completing the performance testing, the system was mounted on a lightweight high-clearance self-propelled sprayer by remote controlling with an operating width of 6.0 m, and a ground gap of 2.0 m. And the actual operational performance of the sprayer showed that the prediction error of LiDAR for plant height was within 10%, which illustrated the accuracy of the detection system and the reliability of the variable application based on the plant height change. And the variable-rate system could adjust the spray volume in real-time according to the plant height change. The penetration rate of the upper, middle, and bottom layers maintained a relatively stable state (upper > middle > bottom) at variable-rate application and could reach the normal application level. This study can provide new ideas for efficient pests and diseases control of high stalk crops, especially suitable for small fields, and gives a reference for the structural design of new precise variable-rate application machinery and performance optimization of efficient application technology.

High Surface Area Mesoporous Cotton Crop Biochar for Enhanced Confiscation of p-nitrophenol

This research study investigates the efficacy of utilizing biochar obtained from cotton crop residues for the enhanced remediation of p-nitrophenol (PNP). Cotton crop residue-derived biochar (CCB) was prepared using 1N phosphoric acid and subsequently characterized. Batch adsorption experimental analysis were conducted to assess the optimal adsorption capacity of CCB for PNP under various conditions, including temperature, pH, interaction time, initial PNP concentration, and dosage of biochar. The results demonstrated optimal adsorption capacity of 55.38 mg/g at pH 5.0, initial PNP concentration of 100 mg L-1 , and biochar dosage of 200 mg at a room temperature of 298 K. The experimental adsorption data exhibited an excellent correlation with the Redlich-Peterson isotherm model. Analysis of the sorption kinetics revealed that the material's uptake behavior closely followed a pseudo-second-order model. Thermodynamics analysis unveiled that the adsorption of PNP onto CCB was exothermic, spontaneous, and primarily driven by enthalpy. Overall, the findings suggest that CCB exhibits promising potential as an efficient adsorbent for the remediation of aquatic environments afflicted with presence of PNP.

How Can Soil Quality Be Accurately and Quickly Studied? A Review

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms.

Rewiring gene circuitry for plant improvement.

Aspirations for high crop growth and yield, nutritional quality and bioproduction of materials are challenged by climate change and limited adoption of new technologies. Here, we review recent advances in approaches to profile and model gene regulatory activity over developmental and response time in specific cells, which have revealed the basis of variation in plant phenotypes: both redeployment of key regulators to new contexts and their repurposing to control different slates of genes. New synthetic biology tools allow tunable, spatiotemporal regulation of transgenes, while recent gene-editing technologies enable manipulation of the regulation of native genes. Ultimately, understanding how gene circuitry is wired to control form and function across varied plant species, combined with advanced technology to rewire that circuitry, will unlock solutions to our greatest challenges in agriculture, energy and the environment.

Phacelia and Buckwheat Cover Crops’ Effects on Soil Quality in Organic Vegetable Production in a High Tunnel System

Cover crops (CCs) are regarded as beneficial to agricultural practice as an option for soil quality improvement in field production systems. The main goal of this study was to assess the impact of spring phacelia (Phacelia tanacetifolia Benth.) and buckwheat (Fagopyrum Mill.) in a crop rotation (CC–leek–parsley, 2020–2021) on the physicochemical and biological properties of the soil in an organic high tunnel system. Soil analyses involved measurements of bulk density, water capacity, soil aggregation, soil organic carbon (SOC), available soil nutrients, as well as microbial abundance and diversity. Phacelia generated more aboveground biomass (58.2 t fresh matter ha−1) than buckwheat (33.0 t ha−1), and their biomass contained 161 kg N ha−1 and 67 kg N ha−1, respectively. A large quantity of elements, such as N, Ca, P, S, B, and Cu, were found in phacelia biomass. More Mg and Na were found in buckwheat plants. The results showed that CC biomass significantly improved some of the soil physical and chemical properties, such as soil organic carbon stock and wet aggregate stability, and decreased soil bulk density. Cover crop treatments changed the dynamics of soil bacterial and fungus populations in a high tunnel system. Phacelia increased the quantity of ammonifiers and nitrifiers in the soil substantially. Further research with a long-term focus is needed to assess the impact of cover crops on soil properties, soil quality, and subsequent crop yields in high tunnel crop rotation and management systems.

Models of osmotic stress as a tool for proteomics and metabolomics of legume seeds

Drought poses a significant challenge to the sustainable development of modern agriculture and to the achievement of high crop yields. Water deficit causes osmotic stress and triggers plant physiological responses characterized by reduced water potential, diminished stomatal conductance, and decreased photosynthetic efficiency. Long-term adaptation to osmotic stress entails intricate metabolic rearrangements, leading to the accumulation of osmoprotectants, activation of antioxidant systems, and increased biosyntheses of stress-protective proteins. The severity and duration of drought, along with plant genotype and developmental stage, influence the plant response to stress, consequently affecting crop yield and quality. Particularly in the context of legumes, which are crucial for human and animal nutrition, understanding adaptive strategies to water deficit is essential for the cultivation of drought-resistant genotypes, primarily because these crops predominantly thrive in semi-arid regions. Proteomics and metabolomics approaches, in turn, serve as valuable tools, offering critical insights into the molecular dynamics governing plant responses to drought stress. Furthermore, the use of reliable drought simulation models is imperative for the effective evaluation of legume response to water scarcity, aiding the cultivation of drought-tolerant varieties. This review highlights the perspectives of utilizing different osmotic stress models to investigate proteome and metabolome alteration within seeds of food legumes.

Improving the value of planting and breeding waste compost in agricultural applications: A zucchini cultivation case and circular agricultural models analysis

An important way of utilizing agricultural wastes to improve soil quality is the use of planting and breeding waste compost (PBWC). However, the impact of compost on agricultural production systems is unclear. Therefore, in order to study the effect of PBWC on crop growth, soil environment, and circular agriculture, PBWC [sheep manure (SM): tail vegetable (TV): corn straw (CS) = 6: 3: 1] was used based on chemical fertilizer reduction for zucchini production. In zucchini production systems, R9 (chemical fertilizer reduction by 30 % + 9,000 kg ha−1 PBWC) treatment was most effective in improving the quality and yield of zucchini fruits, mainly by improving the soil microenvironment, manifested in (1) increasing soil alkali-hydrolyzable nitrogen (AN) available potassium(AK), and soil organic matter (SOM), reducing EC, and improving C/N. (2) Increasing the richness and metabolic capacity of bacterial community. (3) Increasing the abundance of Proteobacteria, a phylum with absolute dominance (up to 32.46 %). Then, an optimization model of “summer corn − greenhouse zucchini − fattened sheep” was constructed to analyze the effect of changes in fertilizer dosage usage on the whole model. Compared with the local traditional recycling model, the optimized system reduced the ratio of inorganic to organic nitrogen input from 2.8:1 to 1.4:1, increased the recycling rate of nitrogen (N), phosphorus (P), and potassium (K) by 31.28 %, 50.84 %, and 30.25 %, respectively, and the net energy yield ratio (EYR) increased by 1.04. The optimized recycling system had significant economic and environmental benefits, which would become the basis for moving towards more sustainable farming. Overall, the circular agriculture model with PBWC returning as the core achieves sustainable production by improving material recycling and soil microenvironment. Therefore, chemical fertilizer reduction by 30 % combined with 9000 kg ha−1 PBWC can be used in practical production to improve the productivity of the circular agriculture model and achieve high yield and high quality crops.

Irrigation scheduling for high value vegetable crops grown under protected cultivation in the hilly ecosystem of north-east India

Irrigation scheduling plays a vital role in the efficient utilization of water to obtain optimum yield, particularly for high value crops grown under protected conditions. The present experiment was conducted during 2020–22 at Horticulture Experimental Farm, ICAR-Research Complex for North Eastern Hill Region, Umiam, Meghalaya to standardize the irrigation scheduling (IS) (5, 7, and 10-days interval) in high-value vegetable crops [tomato (Solanum lycopersicum L.), capsicum (Capsicum annuum L.) and king-chilli (Capsicum chinense Jacq.)] grown under naturally ventilated low-cost polyhouse in the mid-hill conditions of Meghalaya. The results revealed a significant effect of IS on plant growth (reduction in plant height, leaf area) and yield attributes (fruit setting, fruit size, fruit weight and total yield) of the crops with an increase in level of moisture stress. The highest yield of tomato (82.7 t/ha), capsicum (40.8 t/ha) and king-chilli (11.8 t/ha) was recorded in the 5-day interval of IS and on increasing the IS interval to 7-days, the yield decreased by 28.7, 36.1 and 35.7%, respectively. Among the crops, capsicum was found to be more sensitive to water stress, followed by king-chilli and tomato. Among the crops, water productivity with respect to irrigation water was maximum in tomato (60.0 g/kg) followed by capsicum (17.2 g/kg) and king-chilli (10.0 g/kg). Among the varieties grown, Megha Tomato-3 of tomato, hybrid Mahabharat of capsicum and landrace Red Long of king-chilli were found superior for growth and yield attributes at different levels of moisture regime. The farmers of the region can get higher yield using identified varieties with irrigation at 5-days intervals.

How photosynthetic performance impacts agricultural productivity in hybrid cotton offspring

Currently, heterosis is an effective method for achieving high crop quality and yield worldwide. Owing to the challenges of breeding and the high cost of the F1 generation, the F2 generation is considered the more desirable hybrid offspring for agricultural production. The use of OJIP fluorescence provides rapid insights into various photosynthetic mechanisms. However, OJIP fluorescence has not been previously studied as an indicator of the rate of heterosis. Consequently, we investigated the relationship between photosynthetic characteristics and growth and developmental parameters in hybrid cotton cultivars. The findings showed a gradual decline in the photosynthetic performance of hybrid cotton as the number of generations increased. In comparison to the F3 generation, both the F1 and F2 generations showed minimal variations in parameters, thus maintaining hybrid dominant and emphasizing the agricultural production potential of the F2 generation. The JIP-test revealed significant differences in the relationship between ψEo and ϕEo parameters, as well as variations in the connections between the photo-response center and electron transfer efficiency, and between cotton yield and fiber quality in the hybrid progeny. These variations can serve as indicators for predicting the extent of hybrid dominance in cotton. The results indicated significant differences in the light and dark responses of the hybrid offspring. By using parents with similar photosynthetic performance as genetic resources for crossbreeding, the photosynthetic capacity of the hybrid progeny can be enhanced to facilitate the efficient absorption and conversion of light energy in crops.

Crop Concentration and Diversification in Latur District of Maharashtra: A Case Study

The present study is an attempt to explain the crop concentration and diversification in agriculture of the Latur district of Maharashtra. Here, a detailed study has been done to know the dominant crop as well as the ranking of the crops in the cropping pattern of the study area. The study is based on secondary data sets that have been collected from the district statistical handbook 2020-21. Using Bhatia’s method, the crop concentration indices for all blocks of the district have been calculated for crops like Soybean, Sugarcane, Tur, Gram, Jowar, Mung and Udid. Crop concentration and Crop diversification indices have been calculated by using index for all the tehsil of the district. This study attempted to delineate the tehsils of high and low crop diversification and crop concentration.

Crop recommendation and forecasting system for Maharashtra using machine learning with LSTM: a novel expectation-maximization technique

Agriculture in Maharashtra has immense importance in India, acting as the back-bone of the economy and a primary livelihood source for a significant population. Being the third largest state in India, Maharashtra has a high scale crop production in the country which also has an important impact on the economy. Initially the study focus on developing predictive models that guide farmers in selecting suitable crops for the divisions in the state of Maharashtra. This study presents a Crop Recommendation System (CRS) designed to support Maharashtra’s agricultural sector by utilizing a comprehensive dataset from 2001 to 2022 provided by the India Meteorological Department. This study helps in improvising technical efficiency and productivity of the farmers. Harvesting crops in optimal condition can help to produce efficient harvest hence the research concentrates on providing best crop recommendation system (CRS) with the help of Machine Learning and Deep Learning techniques. The data, enhanced for accuracy using expectation-maximization optimization, underpins predictive models that guide crop selection. EM contributes to a more robust and reliable dataset for subsequent analyses and modeling by iterative estimating and updating missing values based on probabilistic expectations. Key findings show that the Random Forest algorithm excels in predicting suitable crops with 92% accuracy. Further precision is achieved through a Long Short-Term Memory network forecasting weather patterns three months ahead, accommodating temporal data variations. Subsequently, the proposed system leverages these forecasts to recommend five ideal crops per division within Maharashtra, aiding farmers’ decision-making and adapting to regional climatic conditions. A supplementary crop calendar offers monthly district-specific planting guidance. An intuitive Graphical User Interface delivers this information effectively, ensuring practical and informed agricultural choices across the state. In essence, the study provides an innovative tool for enhancing economic stability and sustenance in Maharashtra through technology-driven agriculture recommendations aligned with future weather expectations.

Genome-wide development of simple sequence repeats markers and genetic diversity analysis of chayote

BackgroundChayote is a high economic crop in the Cucurbitaceae family, playing an important role in food production, disease treatment and the production of degradable materials in industries. Due to the harsh environment, such as high temperature, drought and frost, some chayote resources are gradually disappearing. It is crucial to collect, characterize, and conserve chayote resources. However, the genetic diversity of chayote resources in China has not been studied so far.ResultsIn this study, we collected 35 individuals of chayote from 14 provinces in China. Subsequently, we found 363,156 SSR motifs from the chayote genome and designed 57 pairs of SSR primers for validation. Out of these, 48 primer pairs successfully amplified bands, with 42 of them showing polymorphism. These 42 primer pairs detected a total of 153 alleles, averaging 3.64 alleles per locus. The polymorphic information content ranged from 0.03 to 0.78, with an average value of 0.41, indicating a high level of polymorphism. Based on the analysis using STRUCTURE, PCoA, and UPGMA methods, the 35 chayote individuals were divided into two major clusters. Through further association analysis, 7 significantly associated SSR markers were identified, including four related to peel color and three related to spine.ConclusionsThese molecular markers will contribute to the analysis of genetic diversity and genetic breeding improvement of chayote in the future.

Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI.

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields.

The Development of an Improved Medium for the In Vitro Germination of Corylus avellana L. Pollen

The European hazelnut (Corylus avellana L.) is a self-incompatible, wind-pollinated species of significant economic-productive interest, mainly cultivated between the Mediterranean basin and the Black Sea. Hazelnut breeding programs and high cropping in commercial orchards are necessarily linked to the availability of fertile pollen with wide germinability and high viability. The objective was to develop an improved method to determine the germinability of pollen, comparing the results with those found in the literature. Pollen germinability was firstly evaluated on a wild-type accession using boric acid (H3BO3), calcium nitrate (Ca(NO3)2), magnesium sulfate (MgSO4), potassium nitrate (KNO3), casein hydrolysate and different concentrations of sugars, including sucrose (10, 15 and 25%) and two monosaccharides, glucose (15%) and fructose (15%). The optimal composition included 15% sucrose in a semi-solid medium composed of 1% animal gelatin, containing collagen. This formulation was then tested on three cultivars of commercial interest, evaluating the effect of different concentrations of sucrose. The comparison between different in vitro germination methods showed that the hanging drop technique in a semi-solid medium provides a statistically higher germination index compared to the liquid and solid/agarized techniques. This study proposes an easy-to-use medium for the in vitro germination of Corylus avellana L. pollen.

ASSESS THE KNOWLEDGE LEVEL AND RELATIONSHIP BETWEEN PROFILE OF FARMER ABOUT POLYHOUSE VEGETABLE CULTIVATION PRACTICES

Polyhouse technology offers an excellent opportunity to grow high value crops during off-season with maximum input use efficiency. However, the successful implementation of polyhouse farming requires adequate knowledge about the specific cultivation practices involved. Therefore, the purpose of this study is to evaluate the farmer knowledge about polyhouse vegetable cultivation practices in Southern Rajasthan and also ascertain the relationship between knowledge of farmers with their personal characteristic. The study was carried out in two districts of Rajasthan namely Bhilwara and Chittorgarh. Data was collected through surveys and interviews conducted with farmers in the region. The analysis of the data was done with the help of different statistical tools like frequency distribution, percentage, mean, standard deviation, mean percent score and correlation coefficient. The study clearly shows that majority of respondents (63.00%) were having medium level of knowledge about polyhouse vegetable cultivation practices in study area. They had highest knowledge about the aspect of components of polyhouse and least about nematode infestation in polyhouse and its control methods. There was a significant correlation found between annual income, education level and land holding with knowledge of respondents about polyhouse vegetable cultivation practices.

Lightweight pH-responsive chitosan hydrogel iron fertilizer: Efficient performance, controlled-release, and tomato application

Ensuring efficient iron absorption by crops is crucial in terms of high crop yields and food security. Bio-based materials effectively transport nutrients with minimal environmental impacts. Here, highly efficient lightweight iron fertilizers, CSG@Fe was prepared using chitosan hydrogels (CSG) as the adsorption carrier. Iron release from the various CSG@Fe preparations was slow under neutral conditions but rapid under acidic conditions. The effects of various cross-linking agents were investigated; 1.0 wt% genipin chitosan hydrogels iron fertilizer (GE-CSG@Fe) evidenced an excellent iron adsorption rate and a higher iron loading (326.15 mg/g) than the other two iron fertilizers. Iron release from CSG@Fe materials followed the Korsmeyer-Peppas model. Notably, wheat seed germination and tomato pot experiments revealed that GE-CSG@Fe significantly enhanced germination and growth. GE-CSG@Fe appropriately reduced the soil pH, protected ferrous ions from soil oxidation and rapid fixation, activated the soil microenvironment, and improved nutrient absorption by the roots of crops growing. This work provides a simple and effective strategy for overcoming constraints in the development and application of iron fertilizers and provides insights into the development of green and efficient iron fertilizers with intelligent response properties.

Thermal time and precipitation dictate cereal rye shoot biomass production

ContextMaximizing cover crop biomass production is key to agroecosystem service delivery. Cover crop biomass accumulation is particularly important in corn (Zea mays L.) silage cropping systems due to the high rate of crop biomass removal that leaves the soil surface mostly bare from early fall until planting the following spring. ObjectiveWe evaluated how weather factors affect rye (Secale cereale L.) cover crop shoot biomass in different rotations including corn silage and soybean [Glycine max (L.) Merr.] and their influence on soybean and corn silage yields. MethodsIn a 13-year field study in a corn silage-soybean rotation, we assessed rye biomass and main crop yields when rye was planted after one or both main crops every year. ResultsRye after silage produced 1.98 Mg ha−1 more shoot biomass than rye after soybean. Thermal time (expressed as growing degree days) had a greater impact than precipitation on the shoot biomass of rye cover crop. A threshold of 800 growing degree days (0°C base) in the fall was determined, above which final biomass was not limited by fall thermal time (R2=0.58). Rye biomass was most strongly affected by fall thermal time (below 800 growing degree days) after soybean and by spring growing degree days and precipitation after corn silage. Rye cover crop inclusion did not decrease corn silage and soybean yields in 12 out of 13 years. ConclusionFall growing degree days below a threshold may limit the maximum potential for spring rye cover crop growth, but spring growing degree days or precipitation may prevent maximum growth from occurring when exceeding 800 growing degree days. Planting rye cover crops in both phases of the rotation did not affect cover crop biomass or main crop yields and may hold a greater potential to protect the soil than planting after only one crop in the rotation. ImplicationsCropping systems that allow a rye cover crop to be planted early enough to accumulate 800 growing degree days in the fall have the best potential for high cover crop biomass production.

Nutrient-feeding strategy promotes the growth, biomass, and quality of genotype/variety CIM Megha of Kalmegh

The current study used two harvesting periods (2020-21 and 2021–2022) to investigate the effects of organic and inorganic fertilizers applied to the soil on Kalmegh growth, productivity, and economic viability. The study looked at a variety of growth parameters, including plant height, the number of primary and secondary branches, plant spread, leaf area, dry matter accumulation, and fresh, dry, and seeded herbage output. T6 (RDF + VAM + Vermicompost) produced much higher growth metrics and yields than other treatments, exhibiting consistently better performance. The improved outcomes were attributed to the combined effects of inorganic fertilizers, mycorrhizal association (VAM), and organic supplementation (vermicompost). T2 (RDF + vermicompost) displayed a similar performance. On the other hand, the T7 control group had the lowest values across all metrics. The economic study determined that T6 was the most economically viable choice due to its higher gross and net returns. This emphasizes the importance of coordinated nutrient management in achieving optimal agricultural output and economic sustainability. According to the study's findings, T6, which has a balanced mix of inorganic and organic amendments, is a promising technique for increasing kalmegh variety CIM Megha production. This technique provides higher crops, financial rewards, and improved soil nutrient levels.

Adapting the process based STICS model to simulate phenology and yield of table grapes- a high value fruit crop in a new emerging viticulture zone of South Asia

Plants strongly interact with their environment making phenology a sensitive indicator of climatic variability. Exploring the possibility of early prediction of phenological timings of table grape cultivars has huge socioeconomic benefits for the growers. Various statistical, mechanistic, and theoretical approaches have been used for parameterization of phenology models mainly for wine grapes. The studies on application of process-based models such as STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) particularly for table grapes and non-traditional viticulture zones are rather limited. The objective of this study was to calibrate the STICS model and evaluate its performance to simulate phenology and yield of four table grape cultivars in Pothwar- a new emerging viticulture zone of south Asia. For this, phenological timings, growth and yield responses were evaluated at two locations (Islamabad and Chakwal) for two consecutive vintages (2019 and 2020). Daily weather data, soil characteristics, genotype and crop specific data were recorded for model calibration. The results indicated a high robustness of the model for simulating table grapes phenology and yield in all cases. The model simulations indicated a satisfactory to high model efficiency (ME) of 0.69 to 0.94 with a prediction variability up to 4 days for growth stages, whereas ME for yield and pruning weight was 0.84 and 0.90, respectively. The model provided an efficient decision support tool (DST) though crucial predictions for viticulture industry. For instance, early maturity (up to 10 days), lesser yield (19–34 %) and pruning weight (19–42 %) for the warmer location (Chakwal) than for the colder Islamabad were simulated by the model with a high skill. The proximity of predicted values to observed values indicates that STICS is powerful tool for predicting vine phenology and yield, thus it provides a valuable insight in timely planning vineyards operations at key growth stages and selecting cultivars with higher yield for sustainable viticulture.