Weather Parameters Research Articles

Weather based thumb rule models for formulating the crop insurance schemes for wheat in Punjab

<p>Weather based crop insurance schemes play an important role in helping the farmers recovering their financial losses incurred due to aberrant meteorological parameters. Wheat is a major winter season crop grown in Punjab state. To formulate the weather based “weekly and monthly thumb rule models” for predicting the high yield of wheat, a study with meteorological and crop data (2007–2008 to 2021–2022) was conducted for three major wheat growing locations in the state. The results revealed that ideally the monthly maximum/minimum temperatures/rainfall/sunshine duration during the months of December, January, February and March in the range of 20–23 ℃/5–9 ℃/0–38 mm/5–8 h, 17–20 ℃/3–8 ℃/2–57 mm/4–6 h, 19–25 ℃/5–11 ℃/0–79 mm/5–8 h and 25–30 ℃/10–15 ℃/0–56 mm/8–9 h, respectively are optimum for high yield of wheat. The ideally humid (maximum/minimum relative humidity between 90%–97%/36%–63%) weather from November to February is favourable for optimum growth and development of wheat crop. Similarly, the maximum/minimum temperatures/rainfall/sunshine duration for anthesis and grain filling stage in the range of 14–23 ℃/3–10 ℃/0–55 mm/2–9 h and 18–30 ℃/5–15 ℃/0–26 mm/4–10 h are favourable for high yield of wheat crop. The maximum temperature of >18 ℃ during grain filling stage is optimum for potential yield of wheat. While the abiotic stresses like heavy rainfall, heat stress during grain filling stage are not favourable for the productivity of the crop. So, these critical limits of weather parameters are the basis for providing the weather based insurance to the farmers of the region.<strong></strong></p>

Seasonal Incidence of Major Insect Pests in Mungbean; Vigna radiata (L. Wilczek)

The current studies on seasonal incidence of major insect pests were carried out at Students’ Instructional Farm of Acharya Narendra Deva University of Agriculture & Technology, Kumarganj, Ayodhya (UP), India during Kharif 2022 and 2023. The current study revealed that whitefly, B. tabaci was commenced from the second fortnight of July and touched the peak at 32th (9.8 whitefly per cage) and 35th SMW (9.4 whitefly per cage) in both the year, respectively. The populations of B. tabaci had a significant negative correlation with rainfall (r= -0.699) in Kharif 2022. The jassid had the highest population in 34th SMW (4.6 jassid/cage) in Kharif 2022, while in second year, maximum jassid population of 5.1 jassid/cage was recorded during 33rd SMW. Wind speed had a significant and negative effect (r=-0.804) on the population of jassid during Kharif 2023. Seasonal abundance of thrips showed that, in first year, the thrips activity reached maximum at 33rd SMW (1.2 trips/5 flower buds). In next season, peak population of 1.4 thrips per 5 flower buds was in 32nd SMW. The correlation study during both the season revealed that the sunshine hours (r= -0.683) showed significant and negative effect on thrips population in 2023. Peak pod borer damage was observed in 36th SMW (5.3%) and 35th SMW (6.7%) during 2022 and 2023, respectively. Correlation between pod borer damage and weather parameters showed that wind speed had negative and significant (r= -0.707) effect on borer damage in first year.

EPG and Pasture Larval Count of Gastrointestinal Nematodes are Strongly Influenced by Weather Parameters: Empirical Evidence from Parbhani Marathwada

Aims: the present study was aimed to develop a bioclimato of gastrointestinal nematodes of cattle in Marathwada region of Maharashtra. Study Design: A standard protocol as per published reports was followed for recording of prevalence of GI nematodes from cattle. Place and Duration of Study: The study was conducted at livestock instructional farm of College of Veterinary and Animal Sciences Parbhani during the period February 2022 to January, 2023. Methodology: In all total 253 faecal samples were collected from calves and adult cattle belonging to RK instructional farm, COVAS, Parbhani. Sample collection and processing was done by following standard parasitological procedures and only fresh fecal droppings were used for the laboratory investigation. Results: The study conducted on availability of larvae on pasture revealed the overall prevalence of 61 percent (87 pasture were positive out of 143 examined). The PLC analysis showed the highest distribution of larvae on pasture during winter season i.e. 57.81 %, followed by monsoon season i.e. 57.81 % and 00.0% during summer season. The Pasture larval count observed during different seasons was 23.83, 33.07 and 0.00 per 100 gm, respectively. The abstract information drawn from the bioclimatos plotted for 2000-2021 and its validation with real time data of 2022 ( Feb 2022-Jan 2023) was a) Suitable months for survival of Heamonchus and Oesophagostomum infective larvae on the pasture of the grazing land in year 2022-2023 were Jan - March and June – September and for Trichostrongylus Jan – March and October 2022- January 2023 and b)The climatic data and Bioclimato were plotted from year 2000-2021 and 2022+January 2023 showed the correlation between EPG and PLC. Conclusion: The bioclimatos can be developed and utilized according the regional agro-climatic variations and be useful for devising controlmeasures against gastrointestinal nematodes of livestock.

Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories

High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms, particularly for high precision measurements of cosmic shear – correlated distortions of images of distant galaxies due to weak gravitational lensing caused by the large-scale mass distribution in the Universe. For unbiased measurements of cosmic shear, all other sources of correlated image distortions must be modeled or removed. One such source is the correlated blurring of images due to optical turbulence in the atmosphere, which dominates the point-spread function (PSF) for ground-based instruments. In this work, we leverage data from weather forecasting models to produce wind speeds and directions, and turbulence parameters, that are realistically correlated with altitude. To study the resulting correlations in the size and shape of the PSF, we generate simulated images of the PSF across a ~10 square-degree field of view – the size of the camera focal plane for the Vera C. Rubin Observatory in Chile – using weather data and historical seeing for a geographic location near the Observatory. We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. We observe a strong anisotropy in the two-dimensional 2PCF, which is expected based on observations in real images, and study the dependence of the orientation of the anisotropy on dominant wind directions near the ground and at higher altitudes. The code repository for producing the correlated weather parameters for input to simulations (psf-weather-station) is public at https://github.com/LSSTDESC/psf-weather-station (https://github.com/LSSTDESC/psf-weather-station)

Analyzing temperature, humidity, and precipitation trends in six regions of Thailand using innovative trend analysis

The change of temperature and weather parameters is a major concern affecting sustainable development and impacting various sectors, such as agriculture, tourism, and industry. Changing weather patterns and their impact on water resources are important climatic factors that society is facing. In Thailand, climatological features such as ambient temperature, relative humidity, and precipitation play a substantial role in affecting extreme weather events, which cause damage to the economy, agriculture, tourism, and livelihood of people. To investigate recent serious changes in annual trends of temperature, relative humidity, and precipitation in Thailand, this study used the Mann–Kendall (MK) test and innovative trend analysis (ITA) methods. The MK test showed that all six regions had an upward trend in temperature and humidity index (humidex, how hot the weather feels to the average person), while relative humidity and precipitation showed both upward and downward trends across different regions. The ITA method further confirmed the upward trend in temperature and humidex and showed that most data points fell above the 1:1 line. However, the upward trend in most variables was not significant at the 5% level. The southern and eastern regions showed a significant upward trend in relative humidity and humidex at a 5% level of significance according to the MK test. The output of this study can help in the understanding of weather variations and predict future situations and can be used for adaptation strategies.

Seasonal Incidence of Insect Pests of Mustard (Brassica juncea L.) and their Correlation with Weather Parameters

Seasonal Incidence of Insect Pests of Mustard (Brassica juncea L.) and their Correlation with Weather Parameters

IoT based weather monitoring system for temperature, humidity, rainfall and seismic detection in real time

This paper presents the design and implementation of an IoT-based weather monitoring system enhanced with seismic detection capabilities, employing Node MCU microcontroller and the Blynk mobile application platform. The system incorporates various sensors including a rain sensor, Light light-dependent resistor (LDR), a DHT11 sensor for temperature and humidity measurement, and an accelerometer (MPU6050) for earthquake detection. The hardware components are interconnected with the Node MCU microcontroller, facilitating real-time data acquisition from the sensors. Programming of the Node MCU is conducted using the Arduino IDE, enabling data transmission to the Blynk app via Wi-Fi connectivity. The Blynk app serves as the user interface, allowing seamless visualization and monitoring of weather parameters and seismic activity on smartphones. Key functionalities of the system include periodic data collection from sensors, transmission of sensor data to the Blynk server, and updating of graphical representations in the Blynk app interface. The accelerometer sensor is utilized to detect changes in acceleration, indicative of seismic disturbances. Threshold values are defined to trigger alerts within the Blynk app in the event of potential seismic activity. The implementation undergoes rigorous testing to ensure accuracy, reliability, and responsiveness under diverse environmental conditions. Considerations such as network stability, power efficiency, and data security are addressed to enhance system performance and longevity. The system demonstrates promising capabilities for real-time weather monitoring and seismic event detection, offering valuable insights for disaster preparedness and environmental analysis. IoT-based weather monitoring system integrated with seismic detection provides an effective solution for comprehensive environmental monitoring, leveraging the power of IoT technology and mobile applications for enhanced data accessibility and analysis. Further advancements and optimizations can be explored to extend the functionality and scalability of the system for broader applications in environmental science and disaster management.

Enhancing agricultural automation through weather invariant soil parameter prediction using machine learning

Soil parameters are crucial aspects in increasing agricultural production. Even though Bangladesh is heavily dependent on agriculture, little research has been done regarding its automation. And a vital aspect of agricultural automation is predicting soil parameters. Generally, sensors relating to soil parameters are quite expensive and are often done in a controlled environment such as a greenhouse. However, a large scale implementation of such expensive sensors is not very feasible. This work tries to find an inexpensive solution towards predicting soil parameters such as soil moisture and temperature, both of which are crucial to the growth of crops. We focus on finding a robust relation between the above mentioned soil parameters with the nearby weather parameters such as humidity and temperature, irrespective of the weather. We apply different machine learning models like multilayer perceptron (MLP), random forest, etc. to predict the soil parameters, given the humidity and temperature of the surrounding environment. For all the experiments we have used a custom made dataset, which contains around 9000 datapoints of soil moisture & temperature, ambient humidity & temperature. The data has been collected in an uncontrolled agriculture bed via inexpensive sensors. Our results show that XGBoost regressor achieves the best results with an R2 score of 0.93 and 0.99 for soil moisture and soil temperature data respectively. This suggests very high correlation between the weather parameters and soil parameters. The model also portrayed a very low root mean squared error and mean absolute error of 0.037 & 0.015 for soil moisture and 0.001 & 0.0008 for soil temperature. Our results show that it is indeed possible to find the soil parameters from the corresponding weather, which will have great impact on mass agricultural automation. The dataset has been made publicly available at https://github.com/Nadimulhaque0403/Soil_parameter_prediction_dataset.

Investigating the correlation of analytical data on pesticide residues in fruits and vegetables with local climatic condition

The use of pesticides is increasing steadily, and even though pesticides are essential for food security, they are known for having adverse effects on human health, and the environment. Further, as pesticides are often a reaction to pests, which are influenced by environmental conditions, the environment might influence the use of pesticides—when assuming, that the use is optimized, and adjusted to those conditions. Therefore, it would be helpful to know how environmental conditions influence the pesticide residue levels of fruits and vegetables. In this work, we investigated the correlation between residue levels of ten different pesticides and the weather parameters air temperature, maximum and minimum temperature, wind speed, precipitation, and sun hours using the Pearson correlation coefficient, linear, and polynomial regression. Also, the pesticide residue levels were analyzed regarding outliers. No correlation between the measured residue levels and the weather parameters could be found for most pesticides. However, for Acetamiprid and Fluopyram, a slight correlation between the pesticide residue levels, the air, minimum-, and maximum temperature could be found.The polynomial regression model was better suited to describe the relationship between pesticide residue levels and weather parameters than the linear regression model, but R2 was not higher than 0.069 for any model.

Population dynamics of pod pest complex of pigeonpea

A field experiment was conducted at Main Agricultural Research Station, University of Agricultural Sciences, Dharwad, to study the population dynamics of pod pest complex of pigeonpea. The observations were recorded at fortnightly intervals starting from flower initiation stage upto the harvest of the crop. The peak activity of gram pod borer (3.63 larvae/plant), spotted pod borer (3.63 larvae/plant) and pod bugs (4.62 bugs/plant)was observed during 50th Standard Meteorological Week. The pod damage caused by Helicoverpa armigera (25.02%) and Maruca vitrata (16.77%) was also highest during 50th Standard Meteorological Week. The maximum pod damage by plume moth (12.02%) was noticed at 52nd Standard Meteorological Week. The 52nd Standard Meteorological Week witnessed highest pod fly population (10.80 maggots / 10 pods), per cent pod damage (27.75%), and per cent seed damage (17.51%).The correlation studies of incidence of pod pest to prevalent weather parameters revealed that, gram pod borer and the spotted pod borer were more prevalent as the minimum temperature rose, but pod fly and pod bugs population increased negatively. There was a negative correlation between the population of all pod pest, including gram pod borer, spotted pod borer, pod bugs and pod fly with maximum relative humidity, minimum relative humidity and rainfall.

Climate drives of growth, yield and microclimate variability in multistoried coconut plantation in Konkan region of Maharashtra, India

Long term experiments (2013-14 to 2018-19) were conducted in Regional Coconut Research Station, Bhatye, a representative location of major coconut growing region of Maharashtra (Konkan region) to study the impact of changing weather parameters on growth and yield of 32 years old coconut plants (dwarf x tall, i.e., COD x WCT). Regression based trend analysis of weather parameters was conducted to check the variability of weather parameters over experimentation years. There was a decrease in maximum temperature (r2=0.034) and increase in minimum temperature (r2=0.017) and rainfall (r2=0.393), indicating change in weather parameters. Correlation studies were carried out to understand the interaction between weather parameters and coconut growth and yield. Maximum temperature had a negative impact on growth (-0.02 and -0.58 for number of leaves and annual leaf production) but had a positive impact on yield (0.41, 0.64 and 0.63 for number of bunches, number of buttons and nut yield). Minimum temperature had significant negative effect on annual leaf production (-0.88) and had a positive effect on nut yield per plant (0.95). The effect of relative humidity (morning and evening) was non-significant. Rainfall had its influence on the crop by negatively affecting the number of bunches (-0.10) and nut yield per plant (-0.48), a positively affecting number of buttons (0.08). Further, microclimate in the plantation was compared to an open field, which indicated lower maximum and minimum temperature (-3.4 and -3.1 %) and higher morning and evening relative humidity (1.6 and 1.9 %) in the coconut plantation as compared to the open field.

Responses in the breeding parameters of the collared flycatcher to the changing climate

Global climate change involves various aspects of climate, including precipitation changes and declining surface wind speeds, but studies investigating biological responses have often focused on the impacts of rising temperatures. Additionally, related long-term studies on bird reproduction tend to concentrate on breeding onset, even though other aspects of breeding could also be sensitive to the diverse weather aspects. This study aimed to explore how multiple aspects of breeding (breeding onset, hatching delay, breeding season length, clutch size, fledgling number) were associated with different weather components. We used an almost four-decade-long dataset to investigate the various aspects of breeding parameters of a collared flycatcher (Ficedula albicollis) population in the Carpathian Basin. Analyses revealed some considerable associations, for example, breeding seasons lengthened with the amount of daily precipitation, and clutch size increased with the number of cool days. Parallel and opposing changes in the correlated pairs of breeding and weather parameters were also observed. The phenological mismatch between prey availability and breeding time slightly increased, and fledgling number strongly decreased with increasing mistiming. Our results highlighted the intricate interplay between climate change and the reproductive patterns of migratory birds, emphasizing the need for a holistic approach. The results also underscored the potential threats posed by climate change to bird populations and the importance of adaptive responses to changing environmental conditions.

Markov Chain analysis of rainfall of Coimbatore

Rainfall is considered one of the most important weather parameters which helps in deciding the time of sowing, pest and disease management and harvesting. Markov chain analysis deals with predicting future values based on past values. In the present study, Markov Chain analysis was used to predict the future probability of monthly rainfall and examine the pattern and distribution of rainfall using daily rainfall data from the year 1982 to 2016 (34 years) in the Coimbatore district. This study mainly analysed the probability of rainfall in the Coimbatore district of Tamil Nadu based on Markov chain process. Based on the National Center for Hydrology and Meteorology, the intensity of rainfall per day was categorized and a day is considered as no rain if rainfall was less than 0.1 mm, low rain if rainfall was between 0.1 mm to 10 mm, moderate rain if rainfall was between 10 mm to 20 mm and heavy rain if rainfall was above 20 mm. By calculating the transition probability matrices and steady-state probability matrices for each month based on the conditional probability of rain on a particular day given that rain on the previous day which is to predict the state of rainfall on the next day. This study reported that the availability of water for crop production is higher during the winter, pre-monsoon, the onset of the southwest monsoon, and at the end of the northeast monsoon. There may be a scarcity of water from August to November for agricultural activities. Based on this study, farmers can plan for a better cropping system in advance to get a better yield.

Effect of Weather Parameters on Population Dynamics of Coccinellids in Sugarcane

Study of the population dynamics of coccinellids were carried out from 28th to 12th SMW during 2022-23. Two species Micraspis yasumatsui and Cheilomenes sexmaculata were found active throughout the whole study period. The population of Coccinellaseptempunctata, Propyleadissecta, Coccinella transversalis first appeared from 30thSMW and reach their peak population during 2ndSMW, whereas the population of Micraspis yasumatsuiand Cheilomenes sexmaculata started from 28th SMW and reach their peak during 52nd and 2ndSMW respectively. The coccinellids showed positive and highly significant correlation with relative humidity and negative and highly significance with maximum temperature.

VALIDATION OF KEY PERFORMANCE INDICATORS OF MOBILE TELECOMMUNICATION OPERATORS USING ENSEMBLE MODELS AND ARTIFICIAL NEURAL NETWORKS WITH NIGERIAN COMMUNICATION COMMISSION’S THRESHOLDS

This paper presents the comparative studies among the predictive models used for the validation of the key parameter indicators of mobile network operators with the Nigerian Communication Commission’s threshold. Four Key Performance Indicators were predicted using artificial neural networks and ensemble models which include bagging and LSBoost models. The Key Performance Indicators and weather parameters for six locations in Southwestern Nigeria were employed. MATLAB R2020a was employed to develop the three models. Microsoft Excel was used in the analysis of the dataset. The bagging model gave the best average compliance of 94% and 100% for CSSR and TCH Congestion Rate respectively while the ANN model yielded the best average compliance of 76.7% and 85.2% for DCR and SDCCH Congestion Rate respectively..

Aerial diseases and their management in Cuminum cyminum: A review

Cumin usually known as Jeera and scientifically known as Cuminumcyminum L. is annual and one of the most important spice crop that is moreprone to several diseases among which the aerial diseases cause severelosses in both yield and quality. Aerial diseases are those diseases whichcause de-formalities in aboveground system of the plant and may includestem, leaves, flower, seed, fruit etc. The threats of Aerial diseases areepidemics in crop production, high cost of chemical fungicides anddevelopment of fungicide resistance, climate change, new diseaseoutbreaks and increasing concerns regarding environmental as well assoil health are becoming increasingly obvious. These demand the use ofintegrated aerial disease management strategies for the crop. This articlesummarized the methods for management of aerial diseases in cuminseed production which included legal methods, resistant cultivars,sanitation, and cropping system, biological control, soil amendments andsoil fertility and weather parameters, plant nutrients, and chemical control.Decision support systems developed to predict disease allow choosinggood management methods such as the use of healthy seeds, culturalpractices and biological control agents for each aerial cumin disease. Thecomplexity in interactions between a pathogen and its host, influenced bybiotic and abiotic factors of the environment, build the control of thediseases frequently very hard. However, profound awareness of host-pathogen systems allows constructing integrated pest managementsystems enhances the production of good quantity as well as quality seed.

Ultraviolet Radiation Quasi-Periodicities and Their Possible Link with the Cosmic Ray and Solar Interplanetary Data

In this study, solar ultraviolet (UV) radiation data collected in Riyadh, Saudi Arabia, between 2015 and 2022 were analyzed to explore quasi-periodicities in the UV time series. The power spectrum density analysis revealed several local peaks that exceeded the 95% confidence interval. These peaks included periodicities of 483–490 days, 272 days, 157−162 days, 103−110 days, 64–72 days, 27 days, and 13 days. To investigate the potential influence of space weather parameters on UV radiation, data on cosmic rays, solar radio flux at 10.7 cm (F10.7 cm), the Kp index, and solar wind speed for the same time period were examined. The aim was to identify periodicities in these variables that aligned with those found in the UV radiation data. The analysis reveals that several periodicities observed in the UV radiation spectrum are also present in the spectra of the considered parameters. Prominent periodicities include a 270-day cycle in UV radiation and cosmic rays, as well as periodicities of 72 days, 27 days, and 13 days in all considered variables. Furthermore, 110-day peaks are observed in spectrum of the UV radiation, the Kp index, solar radio flux F10.7, and solar wind speed. Notably, consistent peaks at 157-day periodicity are identified in the UV spectrum, also present in the spectra of all the considered variables (cosmic rays ∼162 days, Kp index ∼162 days, solar radio flux ∼156 days, and solar wind speed ∼163 days). The identification of common periodicities between UV radiation and space weather parameters in this study provides compelling evidence of a potential direct or indirect influence of solar variations on UV radiation. This finding significantly enhances our understanding of the impact of extraterrestrial factors, particularly solar activity, on the Earth’s environment.

Patterns of water use by the Australian native Melaleuca styphelioides in urban environments and comparison of transpiration prediction by three different micrometeorological models

Key messageThe high-quality sap flow dataset of Melaleuca styphelioides could assists local councils in assessing tree water use and aids in the development of an urban tree sustainable planting management planTrees have a vital part to play in urban ecosystems, offering ecological, economic, and social advantages in addition to beautifying our suburbs. Knowledge of tree water use in urban environments is crucial for facilitating urban greening when there is a perception that growing trees on the street verge on expansive soils poses a risk to pavement and buildings. Information on long-term water use by individual trees in an urban environment is essential for local councils in developing urban tree management plans for sustainable planning of trees; however, this information is scarce. Micrometeorological models have commonly been used to estimate the canopy transpiration of plants in the absence of sap flow data. However, the reliability and accuracy of these models have rarely been assessed using sap flow measurements at the field site. This study aimed to provide the water use data of four individual Australian native Melaleuca styphelioides Sm. using sap flow instruments over 25 months. Tree transpiration, estimated based on three commonly used micrometeorological models using weather parameters and tree characteristics, was assessed and compared with sap flow measurements by employing the linear regression statistical analysis. The results revealed that the modified Penman–Monteith (PM) model demonstrated the highest level of accuracy among the evaluated models, consistently yielding lower errors and providing more reliable estimates of tree water use. This suggests that this model may be more appropriate for predicting plant water use in situations where sap flow data are unavailable.

Bionomics and Damage Estimation of Dolichothrips indicus (Hood, 1919) (Thysanoptera: Phlaeothripidae) in a New Host in Tamil Nadu, India

Bionomics and damage of a polyphagous and an enigmatic phlaeothripid insect, Dolichothrips indicus (Hood, 1919) has been described on a profitable and popular agro-forestry crop Melia dubia (Malabar Neem) in Tamil Nadu, a southern state of India. This little-known target thrips species, previously known to feed flowers and shoots of different plant families has come out as a pest of M. dubia which is persistent in nurseries and regular in plantations. Molecular aided taxonomic identification of D. indicus performed during the study revealed their highest compatibility with the type specimens of D. rambhutanae (Ananthakrishnan) and D. pumilus (Priesner). Population assessment and mapping of D. indicus for over a year showed nil to very high population. Population density with individuals <50 had occurred in 3 out of total 23 study locations comprised of 13 districts of Tamil Nadu triggered by positively correlated (r=0.384) relative humidity. Whereas other weather parameters did not impact the population significantly. Alteration in food plant selection from Ailanthus excelsa shoots feeder to ravenous shoot and leaf feeder of host M. dubia was documented. Infected leaves of M. dubia encountered the development of boundless branched stellate trichomes on the abaxial surface. The feeding and egg laying damage of D. indicus on M. dubia resulted the loss of average 52% of chlorophyll content and 80% of total leaf lamina compared to the healthy one after heavy infestation. Other secondary infection symptoms seen in the adaxial surface of leaves during infestation are yet to be investigated.

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.