Optimum Crop Yield Research Articles

A REVIEW: SMART WEED DETECTION AND HEALTH MONITORING AGROBOT

As agriculture becomes increasingly important in ensuring food security for the world’s growing population, there has been a rise in the development of smart agricultural robots to optimize crop yield. One critical area where these robots can make a significant impact is in weed detection and health monitoring, which can have a significant impact on crop yield and quality. This review paper aims to examine the latest research in smart weed detection and health monitoring agrobots. The paper discusses several studies on autonomous agricultural robots that detect and remove weeds from fields using image processing, deep learning, and fuzzy logic-based classification techniques. In addition to weed detection and removal, the review paper also examines research on agrobots that monitor the health of crops. Moreover, the paper also discusses various techniques for path planning and control for autonomous agricultural vehicles. Finally, the review paper analyzes the role of single-board computers such as Raspberry Pi in agriculture. Overall, this review paper presents a comprehensive analysis of the latest research in smart weed detection and health monitoring agrobots. By examining the various techniques, methodologies, and algorithms employed by researchers, this paper offers valuable insights for future research and development in this field.

Sustainable Crop Recommendation System Using Soil NPK Sensor

The effective management of nutrient resources in agricultural practices is crucial for optimizing crop yields and ensuring sustainable farming. Traditionally, farmers have relied on manual methods or expert knowledge to determine the appropriate amount and type of nutrients required by crops. However, these methods often lack precision and can lead to suboptimal fertilization, resulting in reduced productivity and environmental degradation. In recent years, advancements in sensor technology have paved the way for more accurate and efficient crop management systems. One such innovation is the NPK sensor, which enables real-time monitoring of soil nutrient levels. Our proposed system utilizes NPK sensor data to offer personalized fertilization recommendations to farmers. The system integrates sensor technology, machine learning algorithms, and agronomic expertise to provide precise and tailored nutrient recommendations based on the specific requirements of different crops and soil conditions. The system collects data from NPK sensors deployed in the field that includes soil nutrient levels. Machine learning algorithms analyze this data to identify patterns and correlation between nutrient levels and crop performance. By leveraging historical data and agronomic knowledge, the system can generate accurate and timely recommendations for nutrient application. In conclusion, the crop recommendation system presented here offers a novel approach to crop management by leveraging NPK sensor technology and machine learning. By providing accurate and personalized nutrient recommendations, the system has the potential to revolutionize modern agriculture, enhancing productivity while promoting environmental stewardship. Further research and field trials are needed to validate and refine the system’s performance and usability, but the preliminary results show promising potential for the adoption of such system in real-world agricultural settings.

Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning

Optimizing Crop Yield Prediction in Precision Agriculture with Hyperspectral Imaging-Unmixing and Deep Learning

Investigation of the Potential to Introduce Internet of Things (Iot) Based Micro-Climate Controlling System for Salad Cucumber (Cucumis Sativus) in Conventional Protected Houses

Protected agriculture seeks to maximize productivity by manipulating climatic factors such as temperature to create the most favorable environmental conditions for the growth of animals and plants. This study aims to (a) identify the status of middle and small-scale protected house farmers using a questionnaire survey and (b) assess the impact of an IoT-based automation system to address identified issues of middle and small-scale protected house farmers. During the survey, the farmers revealed their inability to leave the polytunnel for at least one day without supervision and the higher labour cost as issues related to crop management. Decreased fruit quality and increased mite attacks, inability to achieve optimum crop yield were the main issues discovered which reduces the crop yield. The experiment was carried out in two protected houses where one house is under an IoT-based micro-climate controlling system and the conventional management in the other. Digital Temperature & Humidity Sensors (DHT21) were used to monitor relative humidity (RH) and temperature, and foggers and exhaust fans were used to control RH and temperature. Capacitance sensors were used to monitor substrate moisture, and a drip irrigation system was used to control it. Salad cucumber plants were grown in grow bags in each protected house. The inside day temperature was reduced by 1.55°C to 4.42°C in the IoT-based protected house and optimum inside day RH conditions were maintained. The maintained micro-climatic conditions showed a positive effect on yield and growth parameters of salad cucumber grown in IoT based protected house. In comparison to a conventional house, the IoT-based protected house had a 41.6% yield increment per vine.

Assessing the Agronomic Subfield Variability by Sentinel-2 NDVI Time-Series and Landscape Position

Optimizing crop yield is one of the main focuses of precision farming. Variability in crop within a field can be influenced by many factors and it is necessary to better understand their interrelationships before precision management methods can be successfully used to optimize yield and quality. In this study, NDVI time-series from Sentinel-2 imagery and the effects of landscape position, topographic features, and weather conditions on agronomic spatial variability of crop yields and yield quality were analyzed. Landscape position allowed the identification of three areas with different topographic characteristics. Subfield A performed the best in terms of grain yield, with a mean yield value 10% higher than subfield B and 35% higher than subfield C, and the protein content was significantly higher in area A. The NDVI derived from the Sentinel-2 data confirms the higher values of area A, compared to subfields B and C, and provides useful information about the lower NDVI cluster in the marginal areas of the field that are more exposed to water flow in the spring season and drought stress in the summer season. Landscape position analysis and Sentinel-2 data can be used to identify high, medium, and low NDVI values differentiated for each subfield area and associated with specific agronomic traits. In a climate change scenario, NDVI time-series and landscape position can improve the agronomic management of the fields.

Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art

This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems for pesticide application, irrigation, harvesting, bio-inspired algorithms for the automation of greenhouse processes, energy management, machine path planning and operation of UAVs (unmanned aerial vehicles), resolution of scheduling problems, and image signal processing for pest and disease diagnosis. Additionally, the review investigated the cost benefits of various energy-management and AI-based energy-saving technologies, the integration of photovoltaics and dynamic pricing based on real-time and time-of-use metrics, and the cost benefits of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID (radiofrequency identification) technologies. The review established that commercially viable AI technologies for agriculture had increased exponentially. For example, AI-based irrigation and soil fertilizer application enabled farmers to realize higher returns on investment on fertilizer application and gross returns above the fertilizer cost, higher yields, and resource use efficiency. Similarly, AI image detection techniques led to the early diagnosis of powdery mildew. The precise operation of agricultural robots was supported by the integration of light imaging, detection, and ranging (LIDAR) optical and electro-optical cameras in place of the traditional GPS (geographic positioning systems) technologies, which are prone to errors. However, critical challenges remained unresolved, including cost, disparities between research and development (R&D) innovations and technology commercialization, energy use, the tradeoff between accuracy and computational speeds, and technology gaps between the Global North and South. In general, the value of this review is that it surveys the literature on the maturity level of various AI technologies in smart greenhouses and offers a state-of-the-art picture of how far the technologies have successfully been applied in agriculture and what can be done to optimize their usability.

Effect of Phosphorus and Potassium Fertilizers Application on Soil Chemical Characteristics and Their Accumulation in Potato Plant Tissues

Applications of nutrients are determined by the plant’s nutritional requirements and the soil’s available nutrients; however, the precise nutrient application is difficult. At Assosa, a field experiment was conducted to determine how soil chemical characteristics and accumulation of phosphorus and potassium in potato tissue responded to phosphorus and potassium fertilizers application. The treatments included four potassium levels (0, 100, 200, and 300 kg·K2O ha−1) and six phosphorus levels (0, 46, 92, 138, 184, and 230 kg·P2O5 ha−1). The experiment was set up as a factorial randomized complete block design (RCBD) and was repeated three times. According to the preplanting soil analysis, the cropping area’s soil has a medium CEC, low accessible phosphorus, and very low exchangeable potassium. The application of phosphorus had a highly significant influence on accessible phosphorus, exchangeable potassium, and CEC, the concentration of phosphorus in the leaf and tuber tissue of the potato plant. While potassium had a significant effect on exchangeable potassium, CEC, phosphorus concentration in potato plant tuber tissue, and potassium level in both the leaf and the tuber, the interaction effect of phosphorus and potassium on all of the above-mentioned soil chemical properties, as well as the amount of P and K in potato plant tissues, was observed. The critical potassium level in the leaf blade for optimum crop yield tissue content was determined to be 100 kg·K2O ha−1, while the critical level of phosphorus was determined to be 184 kg·P2O5 ha−1.

VIS-NIR-SWIR Hyperspectroscopy Combined with Data Mining and Machine Learning for Classification of Predicted Chemometrics of Green Lettuce

VIS-NIR-SWIR hyperspectroscopy is a significant technique used in remote sensing for classification of prediction-based chemometrics and machine learning. Chemometrics, together with biophysical and biochemical parameters, is a laborious technique; however, researchers are very interested in this field because of the benefits in terms of optimizing crop yields. In this study, we investigated the hypothesis that VIS-NIR-SWIR could be efficiently applied for classification and prediction of leaf thickness and pigment profiling of green lettuce in terms of reflectance, transmittance, and absorbance data according to the variety. For this purpose, we used a spectroradiometer in the visible, near-infrared, and shortwave ranges (VIS-NIR-SWIR). The results showed many chemometric parameters and fingerprints in the 400–2500 nm spectral curve range. Therefore, this technique, combined with rapid data mining, machine learning algorithms, and other multivariate statistical analyses such as PCA, MCR, LDA, SVM, KNN, and PLSR, can be used as a tool to classify plants with the highest accuracy and precision. The fingerprints of the hyperspectral data indicated the presence of functional groups associated with biophysical and biochemical components in green lettuce, allowing the plants to be correctly classified with higher accuracy (99 to 100%). Biophysical parameters such as thickness could be predicted using PLSR models, which showed R2P and RMSEP values greater than >0.991 and 6.21, respectively, according to the relationship between absorbance and reflectance or transmittance spectroscopy curves. Thus, we report the methodology and confirm the ability of VIS-NIR-SWIR hyperspectroscopy to simultaneously classify and predict data with high accuracy and precision, at low cost and with rapid acquisition, based on a remote sensing tool, which can enable the successful management of crops such as green lettuce and other plants using precision agriculture systems.

Yam Staking Reduces Soil Loss Due to Crop Harvesting under Agronomic Management System: Environmental Effect of Soil Carbon Loss

The staking (elevating creeping vines above the ground with poles) of yam is practiced to optimize crop yield, but its effect on soil loss due to crop harvesting (SLCH-soil adhering to harvested tubers) and its associated carbon loss has not been investigated globally. A 3-year field experiment was conducted to study the yam (Dioscorea rotundata) staking effect on SLCH and to examine the environmental effect of soil carbon loss. Staking reduced soil loss due to crop harvesting by 55.6% and increased yam yield by 33.3% when compared to un-staking. Soil carbon loss and root hair weight per tuber yield decreased by 47.7 and 58.4%, respectively, under staking compared with un-staking practices. The un-staking with higher moisture (≃42%) exported two times more soil-available nutrients (N, P, K and Ca) than staking. SLCH was also linearly related to root hair weight (R2 = 0.88–0.75; p < 0.05) and moisture content (R2 = 0.79–0.63; p < 0.05). The lower ratio of root hair weight to tuber yield coupled with moderate mound moisture in staking neutralized its higher tuber yield effect on SLCH by reducing soil loss and its carbon loss. Thus, yam staking mitigates soil loss and its carbon loss which can increase the sequestration potential of soil carbon stock.

Meta Learning-Based Dynamic Ensemble Model for Crop Selection

ABSTRACT Agricultural sector is working for optimal crop yield toward securing a sustainable food supply for the world. Fast growth in precision agriculture helps farmers to increase their yields by extending the era of machine-learning techniques. However, in organic and inorganic farming, predicting yield is an open issue that dominantly depends on the presence of soil nutrients. The lack of knowledge about the richness of land nutrients deals with the crop selection problem. Therefore, the proposed work extended the idea of a dynamic ensemble model for imbalanced multi-class nutrient data. In this work, an attempt is being made to include a novel customized voting strategy for deciding the final class output from the ensemble model. As an initial step, a well-known ranking technique, VIKOR, is applied over land nutrients to extract the most informative land samples. The rationale is to reduce the complexity of the ensemble model by determining only informative land samples for further classification. Furthermore, the meta-learning approach of dynamic ensemble selection accounts for multi-criterion-based competent classifier selection as meta-classifiers. These meta-classifiers decide on ensemble formation with the customized voting strategy to classify the right crop for the test land. To investigate nutrient richness, real-time soil and water nutrient data are collected from the soil testing laboratory, which covers different spatial data. Our experiments on six popular DES algorithms over nutrient data reveal the proposed algorithm’s outperformance in specificity, sensitivity, BCA, Multi-Area under Curve, and precision. Moreover, the lesser computational time of the proposed work indicates the model’s efficiency toward suitable crop selection.

Vision on the bog: Cranberry crop risk evaluation with deep learning

Computer vision and AI for smart agriculture have exciting potential in optimizing crop yield while reducing resource use for better environmental and commercial outcomes. The goal of this work is to develop state-of-the-art computer vision algorithms for image-based crop evaluation and weather-related risk assessment to support real-time decision-making for growers. We develop a cranberry bog monitoring system that maps cranberry density and also predicts short-term cranberry internal temperatures. We have two important algorithm contributions. First, we develop a method for cranberry instance segmentation that provides the number of sun-exposed cranberries (not covered by the crop canopy) that are at risk of overheating. The algorithm is based on a novel weakly supervised framework using inexpensive point-click annotations, avoiding time-consuming annotations of fully-supervised methods. The second algorithmic contribution is an in-field joint solar irradiation and berry temperature prediction in an end-to-end differentiable network. The combined system enables over-heating risk assessment to inform irrigation decisions. To support these algorithms, we employ drone-based crop imaging and ground-based sky imaging systems to obtain a large-scale dataset at multiple time points. Through extensive experimental evaluation, we demonstrate high accuracy in cranberry segmentation, irradiance prediction and internal berry temperature prediction. This work is a pioneering step in using computer vision and machine learning for rapid, short-term decision-making that can assist growers in irrigation decisions in response to complex time-sensitive risk factors. Datasets collected over two growing seasons are made publicly available to support further research. The methods can be extended to additional crops beyond cranberries, such as grapes, olives, and grain, where irrigation management is increasingly challenging as climate changes.

The Use of PGPB to Promote Plant Hydroponic Growth.

Improvements to the world’s food supply chain are needed to ensure sufficient food is produced to meet increasing population demands. Growing food in soilless hydroponic systems constitutes a promising strategy, as this method utilizes significantly less water than conventional agriculture, can be situated in urban areas, and can be stacked vertically to increase yields per acre. However, further research is needed to optimize crop yields in these systems. One method to increase hydroponic plant yields involves adding plant growth-promoting bacteria (PGPB) into these systems. PGPB are organisms that can significantly increase crop yields via a wide range of mechanisms, including stress reduction, increases in nutrient uptake, plant hormone modulation, and biocontrol. The aim of this review is to provide critical information for researchers on the current state of the use of PGPB in hydroponics so that meaningful advances can be made. An overview of the history and types of hydroponic systems is provided, followed by an overview of known PGPB mechanisms. Finally, examples of PGPB research that has been conducted in hydroponic systems are described. Amalgamating the current state of knowledge should ensure that future experiments can be designed to effectively transition results from the lab to the farm/producer, and the consumer.

Sistem Kontrol Otomatis Suhu dan Kelembapan Pada Budidaya Jamur Tiram Berbasis IOT

Oyster mushroom is a type of food plant that is favored by many people, therefore many people cultivate oyster mushrooms to make a living because they see the large demand for oyster mushrooms, this problem is not matched by sophisticated tools in this cutting edge period. Farmers still use manual tools for mushroom treatment, by making a temperature and humidity monitoring system on mushroom kumbung, farmers can optimize crop yields, the monitoring system built is by installing a DHT 11 sensor around the mushroom kumbung, the sensor can be set by NodMCU and can measure the temperature and humidity in the oyster mushroom kumbung, the temperature and humidity settings are regulated by the NodeMCU which can later be displayed via the Smartphone, the conclusion of this monitoring system is increase the productivity of mushrooms produced by farmers.

Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment

Ascertaining water quality for irrigational use by employing conventional methods is often time taking and expensive due to the determination of multiple parameters needed, especially in developing countries. Therefore, constructing precise and adequate models may be beneficial in resolving this problem in agricultural water management to determine the suitable water quality classes for optimal crop yield production. To achieve this objective, five machine learning (ML) models, namely linear regression (LR), random subspace (RSS), additive regression (AR), reduced error pruning tree (REPTree), and support vector machine (SVM), have been developed and tested for predicting of six irrigation water quality (IWQ) indices such as sodium adsorption ratio (SAR), percent sodium (%Na), permeability index (PI), Kelly ratio (KR), soluble sodium percentage (SSP), and magnesium hazards (MH) in groundwater of the Nand Samand catchment of Rajasthan. The accuracy of these models was determined serially using the mean squared error (MSE), correlation coefficients (r), mean absolute error (MAE), and root mean square error (RMSE). The SVM model showed the best-fit model for all irrigation indices during testing, that is, RMSE: 0.0662, 4.0568, 3.0168, 0.1113, 3.7046, and 5.1066; r: 0.9364, 0.9618, 0.9588, 0.9819, 0.9547, and 0.8903; MSE: 0.004381, 16.45781, 9.101218, 0.012383, 13.72447, and 26.078; MAE: 0.042, 3.1999, 2.3584, 0.0726, 2.9603, and 4.0582 for KR, MH, SSP, SAR, %Na, and PI, respectively. The KR and SAR values were predicted accurately by the SVM model in comparison to the observed values. As a result, machine learning algorithms can improve irrigation water quality characteristics, which is critical for farmers and crop management in various irrigation procedures. Additionally, the findings of this research suggest that ML models are effective tools for reliably predicting groundwater quality using general water quality parameters that may be acquired directly on periodical basis. Assessment of water quality indices may also help in deriving optimal strategies to utilise inferior quality water conjunctively with fresh water resources in the water-limited areas.

Major weeds of Ecuador IV. Coffee plantations in Jipijapa

Coffee is a crop of economic importance in Ecuador, grown in area of 199 215 ha, 38,6% of it in Canton of Jipijapa, located in the south of the province of Manabí. Coffee here is grown by small farmers, who spend who spend little annually for the maintenance of plantations. One of the main constraints for getting optimal crop yields is the presence of weeds, which affect water stress during dry spells, and bring about deficient plant uptake of essential nutrients. Weeds are not usually properly controlled in all areas of this crop. Some farmers eliminate weeds twice or three times a year, others do that only once. The most common control method is hand hoeing, but some growers use foliar-applied herbicides, any of these practices are commonly carried out without knowledge of prevailing weed species. Suitable weed management strategy should include data about main weeds and their biology during dry and rainy seasons in Jipijapa. To this end, in 15 ha of coffee plantations during 2014 dry period and in another 15 ha during 2015 rainy one, weed cover and species present were visually evaluated using a scale 0-5, where 1- up to 5% weed cover and 4-more than 50%. These values were processed to determine Absolute and Relative Frequencies, average weed cover and finally Severity Infestation (SI). A total 39 weed species belonging to 19 families were found in both seasons, mostly broadleaves plants, while grasses were found in less shady spots of the plantations. The prevailing weed during rainy period appears to be Senna occidentalis followed by Commelina erecta, Laportea aestuans, Alternathera flavescens and Xanthosoma spp., while during dry season, the fern Pteridium aquilinum was the leading species followed by perennials Alternathera flavescens and the C4 grass Megathyrsus maximus. The species found only responded to the humidity conditions in each season and not to the effect of a certain control measure. Most of the evaluated plantations show poor crop management, scarce crop plant density, and even coffee planted in favor of the slope. Improved plantation management is the key measure to reduce the weed presence of weeds. In the rainy season, with more weed abundance, two early weeding operations, with an additional one later are advisable, while in the dry season, in the case of plantations with adequate shade, one weeding may satisfactorily reduce the weed presence. Perennial Geophila macropoda, found during rainy season, is a potential living cover in coffee plantations since it may help to reduce soil erosion and smothers weeds.

Effects of Farmyard Manure and Different Phosphorus Inorganic Fertilizer Application Rates on Wheat Cultivation in Phosphorus-Deficient Soil

Less phosphorus (P) availability in calcareous alkaline soils is one of the major problems in achieving an optimum crop yield. Its deficiency in plants adversely affects growth and yield attributes. To overcome this issue, growers incorporate inorganic P fertilizers. However, the need for time in the sustainable management of soil fertility in terms of P. Farmyard manure (FYM) application is one of the most popular organic amendments in this regard. Thus, the current study was conducted to explore the best application rate of FYM in combination with inorganic P fertilizer single super phosphate (SSP). There were six treatments i.e., control (0F), 100%SSP (100P), 25% FYM and 75% SSP (25F+75P), 50% FYM and 50% SSP (50F+50P), 75% FYM and 25% SSP (75F+25P), and 100% FYM (100F+0P), applied in three replications. The design of the experiment was a randomized complete block design. For assessment of treatment response, two wheat cultivars (V1 = Pirsabak and V2 = Atta Habib) were used. Results showed that the application of 50F+50P significantly improved the plant height (20.69 and 32.01%), spike/m2 (35.19 and 30.10%), grain (41.10 and 38.16%), and leaf P (49.82 and 71.32%) compared to control in V1 and V2, respectively. A significant improvement in the grain and the biological yield of wheat V2 also validated the efficacious functioning of 50F+50P over control. In conclusion, 50F+50P has the potential to enhance wheat growth and nutrient concentration over control. More investigations are required for a more precise and balanced synchronization of FYM and SSP for the achievement of maximum wheat yield.

Effect of Irrigation Scheduling at Different Phonological Phases on Growth and Yield of Mustard (Brassica juncea l.) in Northern Telangana

Optimum crop yield is not possible without the timely application and right amount of irrigation water. Since rainfall during rabi is inadequate and uncertain, scheduling of irrigation during critical stages could boost the growth, yield and water productivity of mustard. So, in order to identify critical stages of irrigation for mustard in Northern Telangana Zone, a field experiment was conducted during rabi, 2021-22 at Agricultural college, Jagtial to investigate the effect of irrigation scheduling at different phenological phases on growth and yield of mustard. The experiment was laid out in randomized complete block design with twelve treatments and replicated thrice. Treatments includes two irrigations each at vegetative and flowering stage (T1), two irrigations each at vegetative and siliqua development (T2), two irrigations each at flowering and siliqua development (T3), three irrigations each at vegetative, flowering and siliqua development (T4), three irrigations each at vegetative, pre-flowering and siliqua development (T5), three irrigations each at pre-flowering, siliqua initiation and siliqua development (T6), four irrigations each at vegetative, pre-flowering, flowering and siliqua development (T7), four irrigations each at vegetative, pre-flowering, flowering and siliqua initiation (T8), four irrigations each at vegetative, pre-flowering, siliqua initiation and siliqua development (T9), four irrigations each at pre-flowering, flowering, siliqua initiation and siliqua development (T10), five irrigations each at vegetative, pre-flowering, flowering, siliqua initiation and siliqua development (T11) and scheduling of irrigation at IW/CPE ratio of 1.0 (T12). The results of the experiment revealed that maximum plant height (146 cm), LAI (0.89), dry matter accumulation (416 g m-2), number of siliquae plant -1 (124), number of filled seeds siliquae-1 (13.65), seed yield (1113 kg ha-1) and stover yield (2224 kg ha-1) were recorded with five irrigations each at vegetative, pre-flowering, flowering, siliqua initiation and siliqua development (T11). However, it was on par with irrigation scheduled at IW/CPE ratio of 1.0 (T12). While, the lowest plant height (102 cm), LAI (0.42), dry matter accumulation (198 g m-2), number of siliquae plant -1 (63), number of filled seeds siliquae-1 (10.43), seed yield (431 kg ha-1) and stover yield (923 kg ha-1) was recorded with scheduling two irrigations at vegetative and siliqua development (T2).

Effect of Irrigation Scheduling at Different Phonological Phases on Growth and Yield of Mustard (Brassica juncea l.) in Northern Telangana

Optimum crop yield is not possible without the timely application and right amount of irrigation water. Since rainfall during rabi is inadequate and uncertain, scheduling of irrigation during critical stages could boost the growth, yield and water productivity of mustard. So, in order to identify critical stages of irrigation for mustard in Northern Telangana Zone, a field experiment was conducted during rabi, 2021-22 at Agricultural college, Jagtial to investigate the effect of irrigation scheduling at different phenological phases on growth and yield of mustard. The experiment was laid out in randomized complete block design with twelve treatments and replicated thrice. Treatments includes two irrigations each at vegetative and flowering stage (T1), two irrigations each at vegetative and siliqua development (T2), two irrigations each at flowering and siliqua development (T3), three irrigations each at vegetative, flowering and siliqua development (T4), three irrigations each at vegetative, pre-flowering and siliqua development (T5), three irrigations each at pre-flowering, siliqua initiation and siliqua development (T6), four irrigations each at vegetative, pre-flowering, flowering and siliqua development (T7), four irrigations each at vegetative, pre-flowering, flowering and siliqua initiation (T8), four irrigations each at vegetative, pre-flowering, siliqua initiation and siliqua development (T9), four irrigations each at pre-flowering, flowering, siliqua initiation and siliqua development (T10), five irrigations each at vegetative, pre-flowering, flowering, siliqua initiation and siliqua development (T11) and scheduling of irrigation at IW/CPE ratio of 1.0 (T12). The results of the experiment revealed that maximum plant height (146 cm), LAI (0.89), dry matter accumulation (416 g m-2), number of siliquae plant -1 (124), number of filled seeds siliquae-1 (13.65), seed yield (1113 kg ha-1) and stover yield (2224 kg ha-1) were recorded with five irrigations each at vegetative, pre-flowering, flowering, siliqua initiation and siliqua development (T11). However, it was on par with irrigation scheduled at IW/CPE ratio of 1.0 (T12). While, the lowest plant height (102 cm), LAI (0.42), dry matter accumulation (198 g m-2), number of siliquae plant -1 (63), number of filled seeds siliquae-1 (10.43), seed yield (431 kg ha-1) and stover yield (923 kg ha-1) was recorded with scheduling two irrigations at vegetative and siliqua development (T2).

Relevance of extension advisory services in climate smart agriculture : A review

Study was conducted to explore the role of extension advisory services in relation to smart agriculture to mitigate the effect of climate. Agriculture is a very prominent sector of the Indian economy accounts for (49%) of the employment and one-sixth of the GDP. The agricultural industry is supported by 500 million small holder farms. Agriculture is extremely vulnerable for climate change and water as well. Climate change affects agriculture in the Asia and Pacific region particularly in those areas that are vulnerable to natural disasters. Higher temperature reduce yield of desirable crops while encouraging weeds and pests proliferation. Changes in precipitation pattern and floods affect agriculture causing crop failures. In India, significant negative impacts occurred due to climate change reasoned to reducing the yields. In addition to advisory services advanced information on likelihood of weather help to carry out to proper management of resources for agricultural operations to minimize the risk and facilitate growth and realizes the optimum crop yield. Further, decentralization of extension services tracing the different state or district-level funding is needing exercise not always known at national level in various states or provinces spending on extension service. It plays a prominent role by creating awareness regarding adaptation of best practices regarding climate change, deforestation and planting of trees also potential to transform the vision of India’s famers about food security, poverty alleviation and sudden climatic threats. Thus, the advisory services help taking the advantage of benevolent weather and minimize adverse impact of malevolent weather.

Effect of Different Levels of Zinc and Compost on Yield and Yield Components of Wheat

Management of organic matter and micronutrients is very important for the sustainable improvement of soil health. Poor soil organic matter usually results in lower availability of zinc (Zn) micronutrients in plants. Such deficiency in Zn causes a significant decrease in the growth and yield of crops. The need at the current time is to balance the application of organic amendments with Zn micronutrients to achieve optimum crop yields. Thus, the current study was conducted to investigate wheat, using compost as organic matter and Zn as a micronutrient. There were three levels of compost (i.e., control (0C), 5 t/ha (5C) and 10 t/ha (10C)) and four levels of Zn (control (0Zn), 2.5 kg Zn/ha (2.5Zn), 5.0 kg Zn/ha (5.0Zn) and 10.0 kg Zn/ha (10.0Zn)) applied with three replicates. The addition of 10C under 10.0Zn produced significantly better results for the maximum enhancement in plant height (8.08%), tillers/m2 (21.61%), spikes/m2 (22.33%) and spike length (40.50%) compared to 0C. Significant enhancements in 1000-grain weight, biological yield and grain yield also validated the effectiveness of 10C under 10.0Zn compared to 0C. In conclusion, application of 10C with 10.0Zn showed the potential to improve wheat growth and yield attributes. The addition of 10C with 10.0Zn also regulated soil mineral N, total soil N and extractable soil P. Further investigation is recommended with different soil textures to verify 10C with 10.0Zn as the best amendment for the enhancement of wheat yield in poor organic matter and Zn-deficient soils.