Farming Conditions Research Articles

Understanding the Spatiotemporal Dynamics and Influencing Factors of the Rice–Crayfish Field in Jianghan Plain, China

The rice–crayfish co-culture system, a representative of Agri-aqua food systems, has emerged worldwide as an effective strategy for enhancing agricultural land use efficiency and boosting sustainability, particularly in China and Southeast Asia. Despite its widespread adoption in China’s Jianghan Plain, the exact spatiotemporal dynamics and factors influencing this practice in this region are yet to be clarified. Therefore, understanding the spatiotemporal dynamics and influencing factors of the rice–crayfish fields (RCFs) is crucial for promoting the rice–crayfish co-culture system, and optimizing land use policies. In this study, we identified the spatial distribution of RCF using Sentinel-2 images and land use spatiotemporal data to analyze its spatiotemporal dynamics during the period of 2016–2020. Additionally, we used the Multiscale Geographically Weighted Regression model to explore the key factors influencing RCF’s spatiotemporal changes. Our findings reveal that (1). the RCF area in Jianghan Plain expanded from 1216.04 km2 to 2429.76 km2 between 2016 and 2020, marking a 99.81% increase. (2). RCF in Jianghan Plain evolved toward a more contiguous and clustered spatial pattern, suggesting a clear industrial agglomeration in this area. (3). The expansion of the RCFs was majorly influenced by its landscape and local agricultural conditions. Significantly, the Aggregation and Landscape Shape Indexes positively impacted this expansion, whereas proximity to rural areas and towns had a negative impact. This study provides a solid foundation for promoting the rice–crayfish co-culture system and sustainably developing related industries. To ensure the sustainable development of rice–crayfish co-culture industries in Jianghan Plain, we recommend that local governments optimize the spatial layout of rural settlements, improve transportation infrastructure, and enhance regional agricultural water sources and irrigation system construction, all in line with the national strategy of rural revitalization and village planning. Additionally, promoting the concentration and contiguity of RCF through land consolidation can achieve efficient development of these industries.

Comparative effects of drought stress on three olive cultivars focusing on older leaves

Drought is one of the main abiotic stresses to which plants are exposed during their growth and development under both natural and agricultural conditions. Water shortage can seriously affect also xerophytic species such as olive (Olea europaea L.), despite the numerous anatomical, morphological, and physiological adaptations to drought developed over time. Until now, the olive physiological responses to drought stress have been studied mainly on young leaves, i.e. on leaves formed after or during water stress, while very few studies have been conducted taking into consideration leaves already existing before stress (older leaves). In this work, in order to investigate the effect of drought on 1-year-old leaves (old leaves), young plants of three Italian olive cultivars, namely ‘Degli’, ‘Leccino’, and ‘Maurino’, were subjected to moderate water stress (30 days irrigated with 10 % of the pot capacity, T1); subsequently, the same plants were exposed at severe water stress (30 days, without water supplying, T2). The results indicate that the stress tolerance of cultivars can be due to several favorable morpho-anatomical traits, such as higher percentage of palisade tissue (about 50 %) and uniform size of starch grains pre and post stress event. ‘Maurino’ stood out for its ability to cope with the imposed drought condition, maintaining an acceptable water status all over the experiment, and sustaining vegetative growth. Moreover, this olive cultivar showed a stability in the size of starch inclusions, all over the experiment. On the contrary, ‘Degli’ and ‘Leccino’ showed several negative changes under drought conditions, i.e. less growth of main branches, smaller starch grains size, marked reduction of photosynthetic activity, and reduction of leaf succulence, showing a less adaptive behaviour to water stress. Our work provides a more complete insight on olive older leaves response to drought.

Prediction of the harmfulness of bristle species in agrophytocenoses with spring barley

Relevance. The appearance of weeds in the fields of farms does not always imply their complete destruction, since from an economic point of view this may be ineffective. Therefore, in order to predict the effectiveness of crop contamination control in specific soil and climatic conditions, an urgent task is to develop and substantiate models of weed harmfulness and calculate economic thresholds for their harmfulness.Methods. The harmfulness of gray bristle (Setaria glauca (L.) Beauv) and green bristle (Setaria viridis (L.) Beauv) in the conditions of the Agro-Invest Plus LLC enterprise in the Karsunsky district of the Ulyanovsk region in 2021–2022 on the yield of Despina barley was studied. The economic harmfulness thresholds were calculated against the background of the use of herbicides Ovsyugen Super, CE — 0.5 l/ha; Axial 50, CE — 0.9 l/ha; Puma Super, EMV — 0.9 l/ha; Avantik Turbo, MD — 0.5 l/ha. The scheme of the experience included: Option 1 — without weeds (control), 2–6 options — with the number of weeds, respectively, 10 pcs/m2, 20 pcs/m2, 30 pcs/m2, 40 pcs/m2, 50 pcs/m2, option 7 — with natural blockage. The accounting area of the plot is 1 m2, the location is systematic with an offset, the repetition is 6 times.Results. Based on field experiments, predictive models have been developed to determine the harmfulness of weeds, which make it possible to establish economic thresholds for their harmfulness in the conditions of a particular farm. The harmfulness of the bristle according to the linear function was 0.106–0.147 c/ha, depending on the amount (pcs/m2) or 0.322–0.291 c/ha by weight (g/m2). According to prognostic models, the proportion (%) of changes in barley grain yield from contamination ranged from 79 to 85%. Studies have shown that chemical weeding is advisable in the presence of bristles in barley phytocenoses from 8 to 15 pcs/m2.

Using artificial intelligence to improve poultry productivity – a review

Abstract A recent study investigated the potential applications of artificial intelligence (AI) in poultry farming. One area where AI can be helpful is in the early detection of diseases. By analyzing data from various sources, such as sensor readings and health records, AI algorithms can identify potential disease outbreaks or health risks in flocks, allowing farmers to take timely preventive measures. Another area where AI can be applied is in controlling the environmental conditions of farms. By analyzing data from sensors that monitor temperature, humidity, ventilation, and lighting conditions, AI algorithms can help farmers create a comfortable and healthy environment for birds, improving their growth and reducing their stress. AI can also optimize the management of healthcare supplies for poultry. By analyzing the nutritional requirements of birds and the availability and prices of different ingredients, AI algorithms can help farmers optimize feed formulations, reducing waste and environmental impacts. Finally, the study explored the use of robots in poultry care. Robots can be used for cleaning, feeding, and monitoring individual birds. By automating these tasks, farmers can reduce labor costs and improve the efficiency of their operations. Overall, the study highlights the potential benefits of using AI and robotics in poultry farming, including early disease detection, improved environmental conditions, optimized feed formulations, and increased automation.

Productivity of the ewe's lambs Romanov breed when using a complex of mineral and vitamin supplements in diets

Relevance. It is known that the mineral composition of the feeds of the Republic of Bashkortostan used in animal diets, in terms of calcium, phosphorus, magnesium, copper, cobalt and sulfur, does not meet the needs of animals. The deficiency of minerals is especially high in growing young animals.In this regard, the development and scientific justification of the use of a complex mineral and vitamin feed additive (hereinafter CMVFA) in the diets of growing young sheep, which stimulate digestion and digestibility of nutrients in the diet, thereby increasing productivity, is of particular importance.Methods. To study the effectiveness of feeding KMVKD in the diets of the Romanov breed yarok on the indicators of body weight gain in peasant farming conditions, 4 groups of yarok were formed on the principle of pairs of analogues (10 heads each). Yarki of the control group received in addition to the main diet at the rate of 20 g of monocalcium phosphate and 15 g of premix per head (P81-1-89). The yarks of the experimental groups of KMVKD were introduced into the composition of crushed grain mixtures: made according to recipes No. 1 — for the first, No. 2 — for the second, No. 3 — for the third (35 g per head per day).Results. With a small difference in the intake of substances between the groups, the use of UMVD of 35 g per head per day had a significant impact on body weight gain. During the experiment period, the average daily weight gain in animals of the control group was 165 g, and in the experimental groups, respectively, 174.75 g, 183.52 g and 190.68 g, or more by 5.91%, 11.22% and 15.56% compared with the control. The level of profitability of production in the experimental groups increased by 10.60%, 16.03% and 20.54% compared with peers from the control group.

Sustainable Organic Farming Maturity Model

Organic farming is of the utmost importance in promoting environmentally sustainable agricultural practices, minimizing environmental contamination, and avoiding using chemical fertilizers and genetically modified organisms (GMOs). Even inexperienced and seasoned farmers frequently encounter formidable obstacles when attempting to authenticate their organic farming status by acquiring organic agriculture certification. The current level of agricultural land maturity is accurately assessed by a comprehensive model presented in this study, which also provides a framework for the transition to organic farming standards. These maturity models were formulated through an exhaustive analysis of agricultural standards, an extensive review of pertinent literature, and expert interviews conducted in 15 distinct locations, with each expert holding certification in a minimum of three organic agricultural standards. Identifying characteristics germane to organic standards, integrating them into maturity models, and establishing maturity items and dimensions are also components of the study. The outcome of our investigation is the Sustainable Organic Farming Maturity (SOFaM) model, which consists of five levels and eight dimensions, as well as a standard operating procedure manual for organic agricultural standard certification applications. This model's potential as an assessment instrument for determining the maturity level of agricultural land has been validated by experts who hold credentials in three distinct domains and three locations. The SOFaM model has the potential to function as a paradigm shift in the agricultural sector, streamline the certification process following organic farming standards, and guarantee adherence to predetermined criteria.

Investigating the effectiveness of endophytic fungi under biotic and abiotic agricultural stress conditions

Endophytic fungi play crucial roles in promoting plant growth and enhancing stress tolerance, making them valuable allies in agriculture. This reviewer explores the advantageous roles and implications of endophytic fungi in plant stress tolerance, focusing on hormonal regulation, nutrient uptake, and their management of various abiotic and biotic stresses. Endophytic fungi influence the production of plant hormones such as auxins, cytokinins, and gibberellins; thus, contributing to enhanced growth and stress resilience. They also assist in nutrient uptake, solubilizing minerals, and fixing atmospheric nitrogen; thereby improving overall plant nutrition. This reviewer discusses the mechanism of endophytic fungi’s effectiveness in managing biotic and abiotic stresses, including; high CO2 levels, waterlogging/drought, salinity, high temperatures, salinity, heavy metal stress as well as plant pathogens and parasitic attacks. Furthermore, the bio-control capabilities of endophytic fungi against biotic stresses are highlighted, showcasing mechanisms such as induced resistance, mycoparasitism, antibiosis, and competition. The biological activities of recently isolated compounds and associated endophytic fungi are also discussed. Thus, as research in this field progresses, harnessing the full potential of endophytic fungi holds promise for promoting resilient and sustainable agriculture in the face of changing environmental conditions.

Metabolism and productivity of quails of Estonian breed when feeding the feed additive Krezatsin

The purpose of the work was to establish the metabolism and productivity of quails of Estonian breed when fed with the Krezatsin feed additive. Under the conditions of a quail farm, the feasibility of using the feed additive Krezatsin in the diets of quails of Estonian breed has been scientifi cally substantiated and proven. Its optimal dose in compound feed for quails has been identifi ed. It has been shown that the use of the Krezatsin additive in feeding quails increased the nutritional value of their feeding, had a positive effect on digestibility, the use of dietary nutrients, growth energy, the livability of quails, their egg and meat productivity. The use this additive reduced feed intake per unit of production. The data obtained in the experiment made it possible to theoretically substantiate and recommend additional resources for production to increase the effi ciency of quail production by optimizing the Krezatsin feed additive in their diets. It has been proven that the use of this additive when rearing quails in an amount of 50 mg per 100 g of compound feed contributed to the normalization of metabolism in the body of quails, significantly increased the digestibility of dry matter by 2,25 abs.%, organic matter by 2,16 abs.%, raw protein by 3,38 abs.%, crude fat by 2,67 abs.%, crude fi ber by 2,76 abs.% and nitrogen-free extractive substances by 1,16 abs.%. The tested additive contributed to better nitrogen absorption and use of macroelements diet. Feeding the studied additive as part of the compound feed contributed to an increase in the live weight of quails by 3,4 %, egg productivity by 6,0 %, improved the quality of eggs and reduced the compound feed intake per unit of production by 3,4 %.

Crossbreeding and its implication for small-scale animal agriculture in Africa: Outcomes, both positive and negative, and future prospects

<p>African animal genetic resources are diverse and have been the subject of crossbreeding for decades to improve local livestock and poultry populations. However, the literature on crossbreeding performance has been inconsistent, with many projects failing due to various reasons. This has led to mixed support and criticism for crossbreeding in small-scale animal agriculture. The review examines the achievements, problems, and future prospects for livestock and poultry genetic improvement through crossbreeding in Africa’s small-scale animal agriculture. Community-based Breeding Practices (CBBP) can be seen as a community livestock development strategy that mobilizes local animal genetic resources and boosts smallholder livestock producers’ ability to collaborate in resource-scarce communities. Genome sequencing is seen as the future cornerstone of promoting crossbreeding in Africa, but it should be based on consideration of the socioeconomic context of small-scale animal husbandry and local livestock production conditions. Smallholder farmers, who are the major custodians of local animal biodiversity, have faced challenges such as genotype and environmental interaction, lack of funding, poor laws, and lack of farmer participation. In conclusion, the review highlights the importance of phenomics and genomic prediction in improving animal genetic resources in Africa, but it also emphasizes the need for further research and development in this area. The study suggests that modern breeding technologies (genomics and phenomics) and training of smallholder livestock farmers in improved animal husbandry management practices can be used to enhance food and nutrition security for African rural households. This review examines the effects of crossbreeding through the decades on small-scale livestock farming in Africa, including positive and negative outcomes as well as future implications.<strong></strong></p>

Adaptation of trend analysis over a small-scale region for assessing climate change parameters

ABSTRACT Several natural and anthropogenic activities mainly escalate the demand for water and food and develop implications for their availability. Hence, it is necessary to analyse the natural and man-made changes for managing resources precisely. This study focuses on assessing the trends of climatic factors such as rainfall and temperature using the Mann–Kendall (MK) test and innovative trend analysis (ITA) for the agricultural cropping seasons. The findings revealed that a statistically insignificant increasing trend in rainfall is observed for all three cropping seasons (i.e.) Kuruvai/Samba (June–September), Thaladi/Navarai (October–January), and Sornavari (February–May), while the mean temperature shows a significantly increasing trend for all three cropping seasons. The results obtained using the MK test are compared with those of the ITA test to identify the reliable trend technique for climatic factor analysis of small regions. To manage the impacts of climate change and develop sustainable conditions in agriculture, this micro-level study assists the decision makers to prefer suitable trend analysis techniques and helps in identifying the climate change adaptation strategies for small regions. Also, this study is helpful for rural farmers to increase their adaptability conditions with regard to the climate change impacts as they are the frontline victims of climate change.

Smart Poultry Farm Automation

Our case-study paper entitled "Smart Poultry Farm Automation" aims to enhance the efficiency and productivity of poultry farming through the application of advanced automation technologies. Poultry farming plays a crucial role in meeting the growing global demand for poultry products. However, traditional farming methods often face challenges related to labor intensity, resource management, and monitoring. This project proposes the design and implementation of a comprehensive automation system for poultry farms. The system will integrate sensors, actuators, and IoT (Internet of Things) technologies to automate various tasks such as environmental control, feeding, watering, and health monitoring of poultry. Real-time data collection and analysis will enable farmers to optimize farm conditions, improve bird health, and minimize resource wastage. Key components of the proposed system include environmental sensors for monitoring temperature, humidity, and ventilation; automated feeders and waterers controlled by IoT devices; and health monitoring systems utilizing image processing or sensor data for early disease detection. The system will be designed to provide farmers with remote access and control through a user-friendly interface, allowing them to monitor farm operations and receive alerts or notifications. The expected outcomes of this project include increased productivity, reduced operational costs, improved animal welfare, and better decision-making for farmers. By leveraging automation and IoT technologies, the proposed system will contribute to the modernization and sustainability of poultry farming practices

Развитие интеграционных формирований рыбоводных хозяйств в Белгородской области

Modern commercial fish farming in Russia is mainly represented by small enterprises. However, in the Central Federal District, and above all in the Belgorod Region, objective conditions have now been created that allow for the processes of concentration of production (with its simultaneous specialization) on the basis of the creation of new management and production structures (corporate associations), taking into account regional peculiarities of production placement and cooperative relationships. There are such favorable conditions in commercial fish farming in the Belgorod region. Being in favorable natural and climatic conditions and using modern biotechnics and fish growing technology, favorable opportunities have developed in the Belgorod regions for the introduction of a cluster production system for the cultivation of high–value fish species (sturgeon, salmon). It is important to create well-managed companies with the participation of heads of local authorities and qualified specialists.

Application of Federated Learning for Smart Agriculture System

Federated Learning (FL) presents a ground breaking approach to addressing data privacy concerns while harnessing the power of machine learning in the agricultural sector. This paper explores the application of FL for smart agriculture, examining its potential benefits and implications. FL enables collaborative model training across decentralized data sources, allowing farmers to contribute their data without compromising privacy. In smart agriculture, FL facilitates the development of customized machine learning models for tasks such as crop yield prediction, disease detection, resource optimization, and livestock management. By leveraging data from diverse geographical regions, FL models can provide localized recommendations tailored to specific farming conditions. This paper discusses the significance of FL in enabling data-driven decision-making, promoting sustainable agricultural practices, and fostering collaboration among stakeholders. Furthermore, it explores the challenges and considerations associated with implementing FL in the agricultural sector, including data heterogeneity, communication constraints, and model aggregation. Despite these challenges, FL offers immense potential for revolutionizing agriculture by empowering farmers with actionable insights while safeguarding their data privacy.

Greenhouse gas and ammonia emissions in modelled cereal crop production under Polish agricultural conditions: An example spring barley

Based on the analysis of statistical data, the average area under cultivation and average yields of barley in Poland in 2010–2020 were calculated. Barley is one of the most important cereals grown in Poland. Its cultivation area occupied an average of 920,595 ha in these years, with average yields of 3.66 Mg∙ha−1. Barley is a cereal grown mainly as a spring cereal. The average area under spring species in these years accounted for 95% (875,771 ha) of the total area under this cereal, and the average yield of spring varieties was 3.60 Mg∙ha−1. In order to estimate emissions of greenhouse gases (GHG) and ammonia (NH3), the sources of emissions – inorganic fertilisers, fuel consumption – were analysed for selected barley cultivation technologies, differentiated by yield level, and variant model production technologies were developed to obtain projected yields. Emissions were calculated for individual greenhouse gases (N2O, CH4 and CO2) and they were recalculated according to the greenhouse potential of each gas (GWP – global warming potential) to be able to compare the total amount of greenhouse gas emissions for the analysed variants. Greenhouse gas emissions for cultivation technology ranged from 134.53 to 136.48 kg CO2eq for 1 Mg yield. A more accurate Tier 2 method was used to estimate NH3 emissions, taking into account soil conditions and climate zone. The estimated ammonia emissions from the application of mineral fertilisers were from 0.99 kg to 2.35 kg for 1 Mg yield.

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture.

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

Three-dimensional copula framework for early warning of agricultural droughts using meteorological drought and vegetation health conditions

ABSTRACT This study develops an early warning system for crop yield (CY) failure based on meteorological drought and vegetation health conditions. The framework combines three drought indices – the Standardized Precipitation Evapotranspiration Index (SPEI), standardized Normalized Difference Vegetation Index (stdNDVI), and standardized CY (stdCY) values – using copulas. Datasets for five major wheat-producing cities in Turkey between 2000 and 2022 are used for analysis. Results indicate that the time periods used to calculate SPEI and NDVI are critical in determining agricultural drought and CY conditions. The critical threshold values for SPEI and NDVI, with a 10% probability of causing agricultural drought, are found to be ~0.28 and ~0.42, respectively. Using a three-dimensional copula model resulted in more precise CY simulations than a two-dimensional model. The validation efforts showed that all of the observed CYs fell within the simulated range, indicating the robustness of the methodology in capturing drought impacts on CY conditions.

Innovative Cultivation Practices for Reducing Nitrate Content in Baby Leaf Lettuce Grown in a Vertical Farm

The aim of this research is to introduce innovative cultivation practices that result in reduced nitrate levels in baby leaf lettuce grown under vertical farming conditions while maintaining high productivity. For this reason, three experiments were conducted. The first experiment focused on the impact of two “white” light spectra with a blue:green:red:far-red ratio of 14:32:43:10 (BlowRhigh) and 21:34:36:7 (BhighRlow). The second experiment assessed the effects of two nitrogen supply conditions: sufficient total nitrogen (N15) and limited total nitrogen (N5), and foliar biostimulant application. In the third experiment, the impact of replacing the nutrient solution in the N15 treatment with tap water for an additional 24 h (TW24) on leaf nitrate content was examined. Results from the lighting experiment revealed no significant effects on agronomical parameters or nitrate content between the two light spectra. Reducing nitrogen content in the nutrient solution reduced leaf nitrate content but negatively influenced agronomical characteristics. Biostimulant application and replacing the nutrient solution with water reduced leaf nitrate content compared to the control and positively affected growth. The most favorable outcomes were observed in plants supplied with sufficient nitrogen and foliar biostimulant but also cultivated for an additional 24 h with tap water (Sp-N15-TW24).

Solar Based Smart Irrigation System

This article presents a novel mobile application utilizing modern information and communication technology for agriculture, centered on the Internet of Things (IoT), embedded systems, and unmanned aerial vehicles (UAVs). The proposed agricultural monitoring system, employing Arduino microcontroller boards, Wi-Fi modules, and environmental sensors, facilitates data collection on temperature, humidity, and soil moisture. UAVs gather environmental data across the farm, enabling cloud-based computation of irrigation requirements for each region. Additionally, the system remotely monitors farm conditions, including water needs, through an Android mobile application, aiding farmers. Results indicate the effectiveness of our IoT-based system in reducing unnecessary water irrigation, aligning with smart agriculture goals. As the global population burgeons, the demand for increased food production escalates. Traditional farming methods necessitate extensive labor and water usage. This paper introduces a compact automated agricultural system prototype. Employing directional guides, the hardware enables precise processes such as seeding, humidity sensing, and watering, ensuring optimal crop growth and economic viability. From seed sowing to watering, the entire system operates autonomously. Testing involved integrating the hardware with a scaled-down plantation model to validate functionality. Keywords: Solar Panels, Arduino Uno, Soil Moisture Sensor, Relay, Lithium Battery, PIR Sensor, TP4058 Module, Gear Motor, LDR Sensor, Submersible Pump, Buzzer.

Development of novel egg type chicken variety with improved productivity for rural backyards of India

Rural poultry farming with improved high producing chicken varieties is transforming the rural livelihoods. The present study was aimed at developing a high egg producing variety suitable for backyard poultry farming. A total of 602 female birds representing five crossbreds, i.e. Kadaknath × IWH (KH), PD-3 × IWH (DH), PD-2 × IWH (VH), PD-1 × IWH (CH) and PD-3 × KH (DKH) were evaluated for their performance up to 72 weeks under farm conditions. The traits evaluated were body weights at 8, 16, 20, 40, 52, 64 and 72 weeks age at sexual maturity (ASM), egg production and egg weight at 40, 52, 64 and 72 weeks and egg quality traits. Analysis of variance was used to assess the effect of genotypes on different traits and heterosis was estimated to check the superiority of the crosses over the parent lines. Genotype had significant influence on all the traits. Morphologically, only DKH crossbred had multi-coloured plumage, while all other crosses had white plumage with brown patches. DKH cross produced about 239.2±2.96 eggs annually and also witnessed the highest heterotic gains for all the body weight traits. Also, heterosis for ASM was very low for DKH cross and hence, in desired direction. Based on present findings, the three-way DKH cross owing to its multi-coloured plumage and good performance can be further studied for its potential as a high-producing layer variety in rural backyards.

ASCD: Automatic sensing and control device for crop irrigation scheduling

Traditional agricultural irrigation systems suffer from wasting a lot of water allocated for irrigation through evaporation and drainage. To address water waste and increase crop productivity, irrigation scheduling is used, as irrigation scheduling depends on four elements: soil moisture, soil characteristics, weather conditions, and plant type. This paper proposes a new design for monitoring agricultural conditions and controlling the amount of soil moisture by determining the required irrigation water ratios and the ideal time period for irrigating crops. An intelligent control and monitoring algorithm was created using experimental data for a wide range of plants (for soil moisture from 21 to 80 kPa), where plants were classified into three groups according to their common irrigation starting point. Based on the proposed plant classification, three models were provided, where irrigation ratios as the required parameter, soil moisture, plant type, and time interval as input parameters. This algorithm was used to program a custom-made automatic sensor and control device (ASCD). The ASCD is equipped with two input ports to communicate with two types of soil moisture sensors (a resistive sensor and a capacitive sensor) and with three output ports to drive three types of electronic irrigation valves. The ASCD can read from one sensor and drive one electronic valve at a time, the choice of which is made by the farmer. ASCD based the new algorithm showed high accuracy compared to a number of experimental results. The average absolute relative deviation (AAPD) of the new models and experimental data is 5.46 %. The application of the new algorithm shows a reduction in the amount of water used for crop irrigation during the day versus irrigation at night. On the other hand, ASCD has proven its success in sensing and controlling, and it works automatically and independently.