Crop Output Research Articles

Plant growth promoting rhizobacteria, organic manures, and chemical fertilizers: impact on crop productivity and soil health of capsicum (Capsicum annuum L.) in North Western Himalayan region

Integrated nutrient management practices play a vital role for enhancing the soil productivity and sustainable agriculture production. In vegetable crops, bell pepper (Capsicum annuum L.) is a most important vegetable as well as spice crop grown worldwide. The indiscriminate use of inorganic fertilizers for long period deteriorates productivity as well as soil fertility. Therefore, this study was proposed for an extensive evaluation of different combinations of organic manure, plant growth-promoting rhizobacteria (PGPR) with inorganic fertilizers and their influence on growth, yield, quality, economics, and soil health in bell pepper. This study was carried out on capsicum cv. Solan Bharpur with 15 treatment combinations in randomized block design with three replications. 75% Nitrogen Phosphorus (NP) + vermi compost (VC) and enriched compost (EC)@ 2.5 t/ha + PGPR resulted greatest available nitrogen (420.13 kg ha−1), phosphorus (53.87 kg ha−1), potassium (466.37 kg ha−1), organic carbon content (1.63%), and electrical conductivity (0.91 dSm−1). Similar treatment combination ie T14 obtained greatest values for yield and yield attributing parameters viz. fruit length (7.06 cm), fruit breadth (5.91 cm), pericarp thickness (6.72 mm), number of fruits per plant (28.05), improved yield by 60.89%, and enhanced total soluble solids (5.94 OBrix), ascorbic acid content (182.54 mg/100 g). Our findings revealed that combining organic manures, such as farm yard manure, VC, EC, and PGPRs, with inorganic fertilizers to the extent of 50–75% is good for enhancing the soil health and delivering a benefit:cost ratio of 2.59 along with long-term crop output.

The Role of Prevailing Agrarian Relations in Lower Crop Productivity and Profitability: Evidence from Uttar Pradesh, India

This study analyzes the agricultural performance of two regions of Uttar Pradesh (UP), Eastern UP and Bundelkhand, representing two distinct agrarian systems: the former falling mostly within the Gangetic plains and the latter being dry land. Despite the regional differences, it appears that differences in the levels of productivity and net incomes earned in the case of major crops cultivated are more specific to class than region. The study raises the broader question: What explains the lower crop productivity and output and the higher costs of production and meager returns from cultivation of the lower-class cultivators? The mainstream economic explanations for this question are mostly confined to factors related to technology and state policy. While acknowledging that these factors are important, it is argued that the existing agrarian relations also play an important role in lowering crop productivity and profitability in the study areas and, therefore, these must also be considered in an ideal analytical framework for understanding agrarian change in the countryside.

Implementation Strategy of Tomato Plant Disease Detection using Optimized Feature Extraction Method

Tomato plants normally have a single growing season, during which they develop, bear fruit, and then perish. The species first appeared in Western South America, Mexico, and Central America. In the sixteenth century, they were brought to various regions. They produce self-pollinating yellow blooms. After being pollinated, the blooms turn into fruits, which, depending on the type, might be red, yellow, green, or even purple. Tomatoes are a well-liked element in many recipes, including salads, sauces, and soups. They are high in vitamins A and C, potassium, and antioxidants. They are afflicted by several illnesses that can seriously harm the plant and lower crop output. These illnesses were brought on by a variety of minor inadequacies in the soil, air, and the major. These diseases are produced by a range of mineral deficiencies in the soil, and the air, and their primary causes include insects and fungi. We discovered that machine learning is a potential avenue for detecting these diseases before they spread to the plant. As a result, we thought about using Feature Extraction Methods to optimize the data

Field Testing and Evaluation of a Seven-Row Inclined Plate Planter for Mustard Cultivation

Mustard seed (Brassica nigra) is a crucial oilseed crop extensively cultivated in several countries, especially in South Asia. Enhancing seed planting techniques is essential to optimize crop output and overcome the challenges faced by farmers. Traditional manual planting methods are inefficient and time-consuming, creating obstacles to overall agricultural productivity. To address this issue, a field test and evaluation of a seven-row inclined plate planter for mustard cultivation were conducted. Precision planting, which involves accurate seed placement at desired intervals, is vital for maximizing crop productivity. The study encompassed a comprehensive analysis of soil parameters, including soil type, moisture content, bulk density, and cone index. Various operational parameters such as seed spacing, row spacing, and ground wheel slip were evaluated. The economic aspects, including cost analysis, breakeven point, payback period, and benefit-cost ratio, were considered to assess the financial feasibility of the tractor-operated planter.
 Calibration results revealed variations in seed rates among different rows and hopper capacities, impacting seed placement uniformity. Additionally, the study examined seed breakage under different operational parameters, emphasizing its influence on germination rates. Field performance data included soil moisture (12.61%), bulk density (1.45 g/cc), and field efficiency (82.30%), offering insights into real-world planter operation. The cost analysis determined a favorable economic outlook, with a benefit-cost ratio of 1.2, signifying the economic viability of machine harvesting compared to manual methods.

Identification of wheat plant disease using hybrid model of pre-processing with CNN

Diagnosis is given top priority in terms of farm resource allocation, because it directly affects the GDP of the country. Crop analysis at an early stage is important for verifying the efficient crop output. Computer vision has a number of intriguing and demanding concerns, including disease detection. After China, India is the world’s second-largest creator of wheat. However, there exist algorithms that can accurately identify the most prevalent illnesses of wheat leaves. To help farmers keep track on a large area of wheat plantation, leaf image and data processing techniques have recently been deployed extensively and in pricey systems. In this study, a hybrid pre-processing practice is used to remove undesired distortions while simultaneously enhancing the images. Fuzzy C-Means (FCM) is used to segment the affected areas from the pre-processed images. The data is then incorporated into a disease classification model using a Convolutional Neural Network (CNN). It was tested using Kaggle data and several metrics to see how efficient the suggested approach was. This study demonstrates that the traditional Long-Short Term Memory (LSTM) technique achieved 91.94% accuracy on the input images, but the hybrid pre-processing model with CNN achieved 95.06 percent accuracy.

Enhancing wheat crop production with eco-friendly chitosan encapsulated nickel oxide nanocomposites: A safe and sustainable solution for higher yield

In this work, we looked at using nickel oxide (NiO) nanocomposites with chitosan encapsulation as a nano-primer to improve wheat crop output. A straightforward green precipitation procedure was used to create the nanocomposites, and they were then characterized using several methods. According to the findings, the chitosan-encapsulated NiO nanocomposites possessed a large surface area and were resilient to changes in pH. Following this, wheat seeds were primed with the nanocomposites, and under greenhouse circumstances, the impact on crop growth was assessed. The findings demonstrated that, in comparison to the control group, nanocomposites priming considerably enhanced wheat growth and germination rate up to 99 %. In comparison to untreated plants, the wheat plants treated with the nanocomposites primer had greater plant height i.e. shoot length (11.4 cm) and root length (10.3 cm), leaf area, and biomass accumulation. Further research into the mechanism underlying the priming effect of nanocomposites on wheat growth revealed that the nanocomposites enhanced nutrient absorption, photosynthesis, and stress tolerance in wheat plants. In conclusion, our research shows that chitosan-encapsulated NiO nanocomposites have the potential to improve wheat crop productivity in an environmentally benign and long-term manner, offering a viable strategy for sustainable farming.

Drivers of crop commercialization in central and southern Oromia, Ethiopia

More than 95% of crop production in Ethiopia comes from smallholder farmers operating on a farm size of less than 2 hectares. This implies that agriculture is dominantly operated by smallholders nationally. Agricultural commercialization is believed to improve farmers’ livelihoods; hence, commercializing agriculture in Ethiopia means contributing to the smallholder farmer’s welfare. To do so, a study of participation and level of commercialization and their determinant factors is vital. Hence, this study was initiated to evaluate crop output commercialization status and its determinants in rural Ethiopia. Heckman’s two-step model was employed for the analysis. The mean commercialization index (CI) for the sample was 43.40% while 26% and 35% of households were commercially oriented and subsistent farmers respectively. The decision to participate in crop output marketing was affected by age, market distance, and instrument for the level of farm mechanization negatively. In contrast, it was affected positively by livestock size (TLU), being in mid-highland agroecology, ownership of equines, amount of annual income, and access to market information. The intensity of commercialization was positively affected by annual income, access to market information, and number of oxen; while it was negatively affected by operational land, level of crop diversification, and market distance significantly. Improving the resource endowment, and minimizing transaction costs by improving access to market centers, market information, and means of transportation can further enhance commercialization. Improving access to farm mechanization and enhancing productivity in highland areas are also issues that shall get policy and development practitioners’ focus.

Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review

The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production

Plant growth and stress-regulating metabolite response to biochar utilization boost crop traits and soil health.

The utilization of biochar (BC) as a soil amendment in agriculture has gained significant traction among many farmers and researchers, primarily due to its eco-friendly role in boosting crop output. However, the performance of specific metabolites (e.g., zeatin, melatonin, sucrose, and phenyllactic acid) in the various tissues of sugarcane plant (leaf, stem, and root) and rhizosphere soil-deemed plant growth and stress regulators in a long-term BC-amended field remains poorly understood. Additionally, literature on the shift in soil attributes and crop growth triggered by the strong response of these bioactive compounds to longterm BC utilization remains undocumented. Metabolome integrated with highthroughput sequencing analyses were conducted to identify and quantify the performance of plant growth and stress-regulating metabolites in a long-term BC-amended field. Additionally, we investigated how the response of these compounds to BC-treated soil influences crop traits and soil biochemical properties. We also identified and quantified the performance of pathogenic bacteria and unraveled the association between these compounds and potential plant growth-promoting bacteria. The BC-supplemented soil significantly boosted the crop traits, including brix, sucrose content, and chlorophyll, as well as soil nutrients, such as soil total nitrogen (TN), ammonium (NH4 +-N), and nitrate (NO3 --N). We also noticed that metabolite-deemed plant growth and stress regulators, including melatonin and phenyllactic acid, were enriched considerably in the stem and root tissues of the BC-amended soil. Zeatin in the leaf, stem, and root tissues exhibited the same trend, followed by sucrose in the leaf tissue of the BC-treated soil, implying that the strong response of these compounds to BC utilization contributed to the promotion of crop traits and soil quality. Pathogenic bacteria belonging to Proteobacteria and Acidobacteria were suppressed under the BC-supplemented soil, especially in the root tissue and rhizosphere soil, whereas plant growth-regulating bacteria, mainly Bradyrhizobium, responded strongly and positively to several metabolites. Our finding provides valuable information for agronomists, farmers, and environmentalists to make informed decisions about crop production, land use, and soil management practices. Proper soil assessment and understanding of the interaction between the attributes of soil, BC, and metabolites are essential for promoting sustainable agriculture practices and land conservation.

Growth and metabolism of pea (Pisum sativum) via biostimulants based on greener ZnO nanoparticles

The purpose of this study was to identify the most important physiological and biological effects of green synthesis ZnO nanoparticles at a size of 65 nm, biostimulant (Folcare) and interaction biostimulant ZnO NPs on plant growth and metabolism. As our understanding of biostimulants’ preventive and restorative modes of action has increased, it is critical to maintain the best crop output and quality possible. The reduction of fertilizers must be substituted by strategies that improve the nutrients uptake or their utilization by the plants. New processing methods are required as an efficient green process or an integrated (hybrid) process for different new technologies of interest. The effects of NPs, biostimulant, and combination ZnO NPs biostimulant on plant cell metabolism were examined in cytosol, chloroplast, and mitochondria of cells from the stems, roots, and leaves. The interaction NPs/biostimulant had a beneficial effect on the morphological and physiological indicators of plant health than when nanoparticles and biostimulant are applied separately. Folcare biostimulant coupled with zinc oxide nanoparticles improved pea crops growth. The improved of the quality of pea plants can be explained at least, in part, by increase in antioxidant activities during plant growth phenophase.

Microplastics meet invasive plants: Unraveling the ecological hazards to agroecosystems

The objective of this study was to assess the combined impact of environmental microplastic pollution and biological invasion which represent critical global eco-environmental challenges. The invasion of Solidago canadensis L. and soil microplastic contamination in the agroecosystem pose severe hazards to soil and plant ecology and human health. Oryza sativa L. (rice) was examined after individual and combined exposure to Solidago canadensis L. invasion (SI) and soil polyethylene microplastic contamination (MPc). Comparing the individual and combination treatments to the control, leaf biomass decreased, with varying changes in carbon, nitrogen, and phosphorus. Antioxidant enzyme activity and reactive oxygen species levels were significantly reduced following SI exposure and increased following the combined treatment (SI × MP). In contrast, ascorbate peroxidase and catalase activities were reduced after the combined treatment. Due to the confluence of various abiotic stressors, the combined treatment had a higher impact on leaf metabolites than the singular SI and MPc treatments. However, in comparison, the combined treatment significantly influenced the metabolic profile. In conclusion, the interaction between SI and MPc resulted in significant metabolic alterations. These changes were characterized by shifts in metabolite pools influenced by antioxidant enzyme activities and nutrient content, ultimately enhancing defense mechanisms within rice crops. Consequently, these stressors threaten the food safety, sustainability, and agricultural output of crops. The co-exposure of invasive plants and microplastics sheds light on the bio-ecological risks associated with microplastics in staple foods and offers valuable insights into the phytotoxicity of invasive plants in the presence of polyethylene microplastics.

Effects of Biotic Stresses and Their Mitigation Strategies in Legumes: A Review

Legumes are crucial group of crops that are widely cultivated around the world for their protein rich seed and forage. The major impediments for boosting the output of legume crops are biotic and abiotic stresses. There is an urgent need to lessen the detrimental effects of these stresses on legume crops in order to boost the yield and production of legumes to address the nation’s nutritional security issue. An attempt was made to gather published information on effects of biotic stress and mitigation strategies for developing resistant genotypes to maximize yield. This work was done at Department of Genetics and Plant Breeding, School of Agriculture, Lovely Professional University Jalandhar in collaboration with other institutes. Systematic cum integrative review of research work done in different parts of World, particularly in India was comprehended. The literature search was done during August, 2022- February, 2023. About 150 review and research papers were screened from various data bases like ARCC journals, google scholar, research gate and scopus and 76 papers were used to write this paper. This review article presents a comprehensive documentation of the significant impacts of various biotic stresses on legume crops, along with the corresponding mitigation techniques. It highlights the potential effectiveness of integrating the pre-breeding approach with genomic breeding methods as a strategy for developing high-yielding cultivars that exhibit resistance to biotic stresses. By implementing this approach, legume productivity can be enhanced in regions with favourable growth conditions for these crops.

Crop Water Productivity from Cloud-Based Landsat Helps Assess California’s Water Savings

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California’s Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ETa; mm/d) to determine Crop Water Use (CWU; m3/m2), and 2. Crop Productivity (CP; kg/m2) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant.

IMPACT OF THE AGRICULTURAL CREDIT GUARANTEE SCHEME FUND ON AGRICULTURAL PRODUCTIVITY IN NIGERIA 1981-2020

This study ascertains the impact of the Agricultural Credit Guarantee Scheme Fund on Agricultural Productivity in Nigeria 1981-2020. The ex-post facto research design was adopted for the study. Secondary data obtained from the Central Bank of Nigeria Annual Statistical Bulletin was used for the study. Jarque Bera test statistics was used to check for data normality while the Augmented Dickey Fuller test (ADF) was used to check the stationarity of the data. Johansen co-integration and error correction model was used to estimate the nexus that exists between the variables of the study. Findings from the study revealed that in the longrun, volume of funding to crop sector (FCP) has a positive effect on the output of crops and the effect is statistically significant. Volume of funding to livestock (FCP) is positively signed and in line with a priori expectation. Volume of funding to fishery (FFS) has positive effect on output of fishery within the period under study and this effect is in line with a priori expectation. Finally, volume of funding to forestry (FFT) is positively signed and in line with a priori expectation. The researcher concluded that quality credit to the agricultural sector is very important in both long and shortrun as agriculture plays a vital role in the life of people especially in developing countries such as Nigeria. It was recommended among others that more funding be dedicated by government to improve productivity while policy geared towards improving funding in the shortrun should be aggressively pursued by the government so as to achieve an equilibrium of positive effect of funding in both the long and shortrun period for the four sectors examined.

Future Food Production Prediction Using AROA Based Hybrid Deep Learning Model in Agri-Sector

Policymaking and administration of national tactics of action for food security rely heavily on advances in models for accurate estimation of food output. In several fields, including food science and engineering, machine learning (ML) has been established to be an effective tool for data investigation and modelling. There has been a rise in recent years in the application of ML models to the tracking and forecasting of food safety. In our analysis, we focused on two sources of food production: livestock production and agricultural production. Livestock production was measured in terms of yield, number of animals, and sum of animals slaughtered; crop output was measured in terms of yields and losses. An innovative hybrid deep learning model is proposed in this paper by fusing a Dense Convolutional Network (DenseNet) with a Long Short-Term Memory (LSTM) to do production analysis. The hybridised algorithm, or A-ROA for short, combines the Arithmetic Optimisation Algorithm (AOA) and the Rider Optimisation Algorithm (ROA) to determine the ideal weight of the LSTM. The current investigation focuses on Iran as a case study. Therefore, we have collected FAOSTAT time series data on livestock and farming outputs in Iran from 1961 to 2017. Findings from this study can help policymakers plan for future generations' food safety and supply by providing a model to anticipate the upcoming food construction.

Flower Visitors, Levels of Cross-Fertilisation, and Pollen-Parent Effects on Fruit Quality in Mango Orchards

Pollination is essential for the reproductive output of crops. Anthropogenic disturbance and global pollinator decline limit pollination success, reducing the quantity or quality of pollen. Relationships between the abundance of flower visitors and fruit production are often poorly understood. We aimed to (1) identify and quantify flower visitors in a mango orchard; (2) assess how much of the crop resulted from self- versus cross-pollination at increasing distances from a cross-pollen source in large, single-cultivar blocks of the cultivar Kensington Pride or the cultivar Calypso; and (3) determine how pollen parentage affected the size, colour, flavour attributes, and nutritional quality of fruit. Mango flowers were mostly visited by rhiniid flies and coccinellid beetles. Approximately 30% of the fruit were the result of cross-pollination, with the percentage significantly decreasing with an increasing distance from a cross-pollen source in the cultivar Calypso. Self-pollinated Calypso fruit were slightly larger and heavier, with higher acid and total polyphenol concentrations than cross-pollinated fruit. Our results showed higher-than-expected levels of cross-fertilisation among fruit, although self-pollen was likely more abundant than cross-pollen in the large orchard blocks. Our results suggest the preferential cross-fertilisation of flowers or the preferential retention of cross-fertilised fruitlets, both representing strategies for circumventing inbreeding depression. Growers should establish vegetated habitats to support pollinator populations and interplant cultivars more closely to maximise cross-pollen transfer.

Approximating the global economic (market) value of farmed animals

Understanding the global economic importance of farmed animals to society is essential as a baseline for decision making about future food systems. We estimated the annual global economic (market) value of live animals and primary production outputs, e.g., meat, eggs, milk, from terrestrial and aquatic farmed animal systems. The results suggest that the total global market value of farmed animals ranges between 1.61 and 3.3 trillion USD (2018) and is expected to be similar in absolute terms to the market value of crop outputs (2.57 trillion USD). The cattle sector dominates the market value of farmed animals. The study highlights the need to consider other values of farmed animals to society, e.g., finance/insurance value and cultural value, in decisions about the sector’s future.

IoT Based Smart Agriculture Monitoring System

The population increase, climate change, and shortage of resources are posing hitherto unheard-of difficulties to the agriculture sector. The application of Internet of Things (IoT) technology to agriculture, or "smart agriculture," has become a game-changing response as a result. An IoT-based Smart Agriculture System intended to transform conventional farming methods is presented in-depth in this paper. In order to maximise crop output, preserve resources, and advance sustainable farming practises, the suggested system makes use of sensor technology, data analytics, and real-time monitoring. Numerous sensors for monitoring temperature, nutrient levels, and soil moisture are among the main elements of the Internet of Things-based Smart Agriculture System. These sensors' wireless transmission of data to a centralised cloud-based platform, where sophisticated data analytics algorithms process the data, is done. This platform is accessible to users via Keywords: Internet of Things (IoT), Sensor Technology, Data Analytics, Sustainability, Crop Monitoring, Agricultural Productivity.

Response of field crop seeds to stimulators improve germination and growth

Seeds are the foundation of agricultural production. Whatever other production elements are present, they will not compensate for the poor production that occurs as a result of the use of inferior seeds, which surely leads to a drop in quantity and quality production. To restart the metabolic processes in the seeds that have been prepared for planting, the seed must be hydrated in order to encourage it to germinate, and it will only do so when the right moisture is available. Stimulating seed germination in a short period of time, fighting with the bush that grows alongside the crop, and lowering the amount of pesticides required in control. Therefore, conserving the environment from pollution and lowering agricultural production costs through the use of available technology with outstanding results, particularly for leguminous crops that suffer from the presence of hard seeds. Soaking technology minimizes seeding rates, reduces work and time, and provides a homogeneous field in growth and its reflection on the harvest at one time, as well as facilitating the harvest process. In comparison to other ways, this is the safest for increasing the output and quality of field crops.

Aquaculture monitoring system using machine learning

Aquaculture is the farming of aquatic creatures for food and other purposes. It is a growing industry that provides a significant amount of the world's seafood. Aquaculture management regulates and oversees these farms to ensure that they are operating effectively and sustainably. Machine learning and IoT can play a significant role in aquaculture management. Machine learning can be used to monitor and optimize farm conditions in real time, while IoT can be used to monitor and manage farms remotely. These technologies can help improve yields, reduce costs, and improve sustainability. In this paper, we propose an aquaculture management system using machine learning and IoT for fish and shrimp farms. The system monitors the environment and collects data from sensors placed on the farm. The data is processed using machine learning algorithms to identify patterns and predict problems. The system provides information on the optimum conditions for farming and the best time to harvest. Monitoring various things like temperature and pH and providing feed at the right time is important, which plays an important role in the output of the final crop.