Agricultural Production Systems Research Articles

Artificial Intelligence-Based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment

Despite the recent increase in smart farming practices, system uncertainty and difficulties associated with maintaining farming sites hinder their widespread adoption. Agricultural production systems are extremely sensitive to operational downtime caused by malfunctions because it can damage crops. To resolve this problem, the types of abnormal data, the present error determination techniques for each data type, and the accuracy of anomaly data determination based on spatial understanding of the sensed values are classified in this paper. We design and implement a system to detect and predict abnormal data using a recurrent neural network algorithm and diagnose malfunctions using an ontological technique. The proposed system comprises the cloud in charge of the IoT equipment installed in the farm testbed, communication and control, system management, and a common framework based on machine learning and deep learning for fault diagnosis. It exhibits excellent prediction performance, with a root mean square error of 0.073 for the long short-term memory model. Considering the increasing number of agricultural production facilities in recent years, the results of this study are expected to prevent damage to farms due to downtime caused by mistakes, faults, and aging.

Expressing Added Value in Food Supply Chains

Value-added food supply chains are chains that maintain positive social, environmental, and community values which are integrated into the production process from the primary producer to the final consumer, ensuring economic, social, and environmental sustainability, thus forming a sustainable agricultural and food production system. The evaluation of the value added in each chain is based on indicators, which are a tool to measure changes in the actual situation or to show performance trends in achieving the progress set by the objectives. The presented indicators are based on publicly available statistics and foreign literature that allow measuring value added in specific livestock chains at aggregate level (beef production, pork production and dairy production) and are based on publicly available statistics in the Republic of Slovenia. The need to produce indicators of value added in livestock food supply chains arises from the large differences in structural changes in the sector itself and in the development and economic performance of the various livestock industries.

Effects of climate factors on wheat and maize under different crop rotations and irrigation strategies in the North China Plain

The North China Plain (NCP) is a crucial agricultural region for grain production in China, primarily focusing on wheat and maize cultivation. However, these crop yields are highly vulnerable to fluctuations in temperature and precipitation, with climate change being a significant factor. This study investigates the impact of climate factors on wheat and maize yields in the NCP under various crop rotations and water supply strategies. Using the Agricultural Production Systems sIMulator crop mechanism model, we evaluated the effects of different crop rotations and water supply strategies on wheat and maize yields. A comprehensive analysis of the simulated results determined the yield variation trends and their correlation and sensitivity to different climate factors. The findings revealed that precipitation levels over the past 40 years showed no significant trend, while radiation levels showed a significant decreasing trend, and annual mean maximum and minimum temperatures displayed significant increasing trends. Furthermore, the study found that irrigation practices and crop rotations substantially impact grain yield in the study area, with average yields ranging from 8105.5 kg ha−1 under rainfed conditions to 13 088.8 kg ha−1 under fully irrigated conditions. There was a statistically significant trend of increasing yields for fully irrigated Monocrop-Wheat and decreasing yields for fully irrigated Monocrop-Maize over the past 40 years. Sensitivity analysis also showed that rational rotation and irrigation can reduce grain yield sensitivity to climate change. In conclusion, the prudent use of rotation and irrigation can enhance food production resilience to climate change. These findings have significant implications for developing strategies to optimize crop yields and adapt to climate change in the NCP while considering trade-offs.

T6SS: A Key to Pseudomonas's Success in Biocontrol?

Bacteria from the genus Pseudomonas have been extensively studied for their capacity to act as biological control agents of disease and pests and for their ability to enhance and promote crop production in agricultural systems. While initial research primarily focused on the human pathogenic bacteria Pseudomonas aeruginosa, recent studies indicate the significance of type VI secretion (T6SS) in other Pseudomonas strains for biocontrol purposes. This system possibly plays a pivotal role in restricting the biological activity of target microorganisms and may also contribute to the bolstering of the survival capabilities of the bacteria within their applied environment. The type VI secretion system is a phage-like structure used to translocate effectors into both prokaryotic and eukaryotic target cells. T6SSs are involved in a myriad of interactions, some of which have direct implications in the success of Pseudomonas as biocontrol agents. The prevalence of T6SSs in the genomes of Pseudomonas species is notably greater than the estimated 25% occurrence rate found in Gram-negative bacteria. This observation implies that T6SS likely plays a pivotal role in the survival and fitness of Pseudomonas. This review provides a brief overview of T6SS, its role in Pseudomonas with biocontrol applications, and future avenues of research within this subject matter.

Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman filter for high dimensional linear Gaussian state space models. EnKF methods have also been developed for parameter inference of static Bayesian models with a Gaussian likelihood, in a way that is analogous to likelihood tempering sequential Monte Carlo (SMC). These methods are commonly referred to as ensemble Kalman inversion (EKI). Unlike SMC, the inference from EKI is asymptotically biased if the likelihood is non-linear and/or non-Gaussian and if the priors are non-Gaussian. However, it is significantly faster to run. Currently, a large limitation of EKI methods is that the covariance of the measurement error is assumed to be fully known. We develop a new method, which we call component-wise iterative EKI (CW-IEKI), that allows elements of the covariance matrix to be inferred alongside the model parameters at negligible extra cost. This novel method is compared to SMC on a linear Gaussian example as well as four examples with non-linear dynamics (i.e. non-linear function of the model parameters). The non-linear examples include a set of population models applied to synthetic data, a model of nitrogen mineralisation in soil that is based on the Agricultural Production Systems Simulator, a model predicting seagrass decline due to stress from water temperature and light, and a model predicting coral calcification rates. On our examples, we find that CW-IEKI has relatively similar predictive performance to SMC, albeit with greater uncertainty, and it has a significantly faster run time.

Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs

The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol’ method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs’ impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool.

The effect of different systems of growing tomatoes in a protected area on morphological properties and business results

We are witnessing negative consequences caused by the use of synthetic crop protection products and fertilizers in food production. Interest in the indoor farming of certain plant species has grown significantly. Organic and integrated farming imply the use of natural products, while limiting or completely eliminating the use of synthetic resources. The goal of the research is to study different farming systems in two types of tomatoes and their effect on the variability of certain tomato properties (the number of formed flower trusses, number of fruits per flower truss, and tomato yield). Furthermore, the gross margin of tomato production was calculated. Factor A comprised of different systems of agricultural production: integrated and organic. Factor B comprised two types of tomatoes (cherry and beef). The experiment was set up in a modern indoor space, in controlled microclimate conditions in randomized block design in four replications. The organic system of farming highly significantly affected the studied parameters. The reason for this was the use of a modern growing technology and the approach to the farming itself.

Revelation of Genotype × Environment Interaction in Linseed (Linum usitatissimum L.) Under Conventional and Natural Farming Production Systems in the North-Western Himalayas

The development of superior genotypes for use in plant breeding programmes is significantly influenced by the genotype in environment (G × E) interaction. The effects of G × E complicate the improvement of linseed as an important oilseed crop. The present study aimed at assessing the G × E interaction of 30 linseed genotypes for seed yield traits and oil content under conventional and zero-budget natural farming conditions across four locations for two consecutive years (16 different environments) in the North-Western Himalayan region. The AMMI model was used to estimate G × E interaction in the present study. The highest contribution to the total variance belonged to G × E interaction (34.75%), followed by genotype main effects (34.28%). Based on the IPCA1 scores, the most stable genotypes identified with high mean performance for oil content were KL-257 and Nagarkot, and for seed yield, Giza-7 was the most promising genotype. However, genotypes KL-280, KL-285, and Giza-8 showed specific adaptation to the natural production system environments in both years for oil content at locations of Palampur, Bajaura, and Kangra, respectively. Therefore, these genotypes could be recommended specifically under the natural production system in the respective locations. However, the genotypes with stable oil content did not have stable seed yields as well. None of the genotypes that exhibited high oil content stability also exhibited good seed yield stability. In terms of the environment, Palampur was recognised as a favourable location for oil content based on the above average performance, whereas Kangra and Dhaula Kuan were found to be unfavourable locations. In terms of discriminating ability, the natural production system at Palampur showed the highest discrimination, whereas Dhaula Kuan was revealed as the least discriminating environment. These stable and high oil-yielding genotypes are valuable genetic resources for linseed breeding programs for reduced input conditions and marginal environments.

I-RAT: A discussion support system to rapidly assess economic and environmental impacts of different sugarcane irrigation practices

Water pollution and climate change are among the greatest threats to the iconic Great Barrier Reef (GBR). To improve the GBR’s long-term outlook, making improvements on these fronts is required. Owing to the complex interactions between soils, climate, and farm management, a tool is needed to guide agricultural land managers about which management practice changes are more likely to deliver improved water quality and climate resilience outcomes whilst maintaining profitability. Using concepts drawn from social studies of science and technology, a ‘discussion’ support system (DSS) named Irrigation Rapid Assessment Tool (i-RAT) was developed through participatory processes designed to enhance co-learning from the development of this DSS. i-RAT is a rapid assessment-visualisation tool in an interactive web application (https://i-rat.net) to visualise the impacts of changed irrigation practices for sugarcane farmers and extension staff. Specifically, i-RAT compares these impacts regarding farm economics, water quality, productivity, carbon and nitrogen cycles, and greenhouse gas emission. The core of i-RAT is the sugarcane module from the Agricultural Production Systems sIMulator (APSIM) modelling platform. i-RAT was first developed for the Burdekin sugarcane growing region in Queensland (Australia’s largest sugarcane producing region). Various soil types and management scenarios representing farming practices in the Burdekin region were used for simulations (1971–2021) to generate a datacube. In this paper, we describe how fundamental learnings about more (drought) resilient farming systems and more sustainable irrigation practices can be extracted from i-RAT. Details of the participatory approach with research and industry partners that informed the design and function of i-RAT and how APSIM was set up and parameterised are described. A ‘what-if’ analysis demonstrated the i-RAT features and application.

Cropping sequence affects the structure and diversity of pathogenic and non-pathogenic soil microbial communities

AimsCurrent understanding of how cropping sequence affects pathogen-suppressive microbiomes in soil is limited. We investigated the effects of several cropping sequences from the 2020–2021 growing seasons, including cereals, pulses, and an oilseed, on microbial communities in rhizosphere and bulk soils in two western Canadian field locations.MethodsThe fungi and bacteria were characterized by Internal Transcribed Spacer (ITS) and 16S rRNA gene sequencing, respectively. The QIIME 2™ bioinformatic pipeline was used to measure the diversity and abundance of microbial species. Additionally, the concentration of the soil mineral chemicals, including macro and micro nutrients, was determined by colorimetric analysis.ResultsAscomycota (62.5%) was the most common fungal phylum, followed by Glomeromycota (11.1%), Mucoromycota (8.9%), and Basidiomycota (6.8%). Pseudomonadota (35.0%), Actinomycetota (21.1%), and Bacillota (10.1%) were the three most common bacterial phyla. Fungal OTU richness and phylogenetic diversity were highest in the cereal-pulse cropping sequencing, and bacterial OTU richness was highest in the pulse-oilseed sequences. Fusarium was the fungal genus most commonly associated with cereal-cereal monoculture and least common in the oilseed-pulse cropping sequences. The fungi (Mortierella, Funneliformis, and Diversispora) and bacteria (Rhizobium, Bradyrhizobium, Flavobacterium, and Candidatus) were higher in the cropping sequences involving pulses. The most prevalent bacteria were Streptomyces in cereal-related sequences and Solirubrobacter and Pseudomonas in oilseed-related sequences. Among soil mineral chemicals, nitrate-nitrogen, copper, calcium, potassium, and chlorine were associated with a number of beneficial fungal and bacterial genera but not with pathogenic fungal genera.ConclusionsThe results highlight the consequences of crop species selection in cropping sequences and the management of agrochemicals in the agricultural production system.

Ecological adaptation of smallholders to tropical climate change in the highland zone of South Sulawesi, Indonesia

Abstract. Busthanul N, Demmallino EB, Sultani HR, Lampe M, Ismail A, Yassi A, Daris L, Saudi MY, Zainuddin A. 2023. Ecological adaptation of smallholders to tropical climate change in the highland zone of South Sulawesi, Indonesia. Biodiversitas 24: 5328-5335. Climate change has adversely affected the stability of agricultural production and livelihood systems, threatening the well-being of farmers in Indonesia, particularly small-scale farmers with limited resources. To mitigate the impacts of climate change on agricultural production, innovative approaches are required. Ecological adaptation is one of such approaches which addresses the threat of climate change. However, studies on ecological adaptation in Indonesia have mostly been concentrated on food crops, with limited attention to horticultural commodities in mountainous regions. This study aims to explore how horticulture farmers in the mountainous region of Enrekang District, South Sulawesi, adapt to the effects of tropical climate change. This study employs a post-positivistic approach, to a case study of the impact of climate change on the horticulture farming community in Enrekang District. The study uses a household survey method to gather quantitative data, while focus group discussions and key informant interviews were used to collect qualitative data using semi-structured and unstructured questionnaires. The study's findings revealed diverse ecological adaptation patterns among horticulture farmers, which include adjustments in planting timing, management of pests and diseases, crop rotation, and the use of pumping systems. In addition to these scientifically based strategies, local knowledge also played a crucial role in adaptation processes. Furthermore, to ensure the sustainability of these ecological adaptation strategies, there is a need for the government and scientists to support and empower local communities collectively. This recognition and collaboration are vital for ensuring the resilience and effectiveness of adaptation efforts in mitigating climate change.

From farm to fork: future supply chains need to measure and trade nutrient content.

In the future, 8-10 billion people will need to be fed by our agriculture and food production system. Moreover, currently up to five billion people are already affected by malnutrition including undernutrition, inadequate consumption of micronutrients, and overweight. A healthy and sustainable diet will therefore play a pivotal role in our future, but most food products are traded and consumed based only on techno-functional or gustatory properties. We wish to stimulate a debate on the urgent need for multidisciplinary research and education to realize future diets with enhanced nutritional profiles. In particular, there is a need to better measure and understand the factors that influence the nutrients of food products along the global supply chains.

Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan

Abstract Remote sensing (RS) can efficiently support the quantification of crop water requirements and water productivity (WP) for evaluating the performance of agricultural production systems and provides relevant feedback for management. This research aimed to estimate winter wheat water consumption and WP in the central clay plain of Sudan by integrating remotely sensed images, climate data, and biophysical modelling. The wheat crop was cultivated under a centre-pivot irrigation system during the winter season of 2014/2015. The Landsat-8 satellite data were used to retrieve the required spectral data. The Surface Energy Balance Algorithm for Land (SEBAL) was supported with RS and climate data for estimating the Actual Evapotranspiration (ETa) and the WP for the wheat crop. The SEBAL outputs were validated using the FAO Penman–Monteith method coupled with field measurements and observation. The results showed that the seasonal ETa ranged from 400 to 600 mm. However, the WP was between 1.2 and 1.5 kg/m3 during the wheat cycle. The spatial ETa and WP maps produced by the SEBAL model and Landsat-8 images can improve water use efficiency at field scale environment and estimate the water balance over large agricultural areas.

Development of an assessment-based planting structure optimization model for mitigating agricultural greenhouse gas emissions

Optimization of crop structure is an efficient way to reduce greenhouse gas (GHGs) from agriculture production. However, carbon footprint have rarely been incorporated into previous planting structure optimization models due to the challenges of assessing the spatial and temporal distribution of agricultural carbon footprint for multiple crops in irrigated districts. In addition, previous planting structure models suffered from strong subjectivity in objective function determination, and the obtained non-dominated solution set offered difficulties to decision-makers in selecting specific implementation options. To fill such gaps, an integrated accounting-assessment-optimization-decision making (AAODM) approach was proposed, which remedies the shortcomings of previous crop planting structure optimization models in carbon footprint mitigation, and overcomes the subjectivity of objective function determination and the difficulty in selecting specific implementation options. Firstly, life cycle assessment (LCA) method was used to account for the multi-year agricultural carbon footprints of multiple crops in the irrigation district. The optimization objective functions of planting structure optimization models can then be determined based on the assessment method of carbon footprint influencing factors. Next, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to generate a non-dominated solution set of the optimization model. The optimal planting structure can be finally obtained based on decision making methods by determining the maximum harmonic mean (HM) and knee points (KPs) of the non-dominated solution set. The developed AAODM approach was then applied to a case study of agricultural crop management in Bayan Nur City, China. The results showed that the level of economic development was a key factor influencing the increase in carbon footprint in Bayan Nur City over the past 20 years. The regulation of the level of economic development would significantly influence the agricultural carbon footprint in Bayan Nur City. Moreover, two optimal crop cultivation patterns were provided for decision-makers by selecting solutions from the Pareto front with decision making methods. The comparison results with other methods showed that the solutions obtained by NSGA-II were superior to MOPSO in terms of carbon reduction. The developed AAODM approach for agricultural GHG mitigation could help agricultural production systems in achieving low carbon emissions and high efficiency.

The Role of ICT in Effective Dissemination of Extension Information among Women Livestock Farmers in North West Region of Cameroon

Over the past years, extensive efforts have been made throughout the world to provide female farmers with effective, suitable technology, training, and information. As a result, positive effects are beginning to show in agricultural production and family welfare. But the successes are limited at a time when public sector investments in agricultural research and extension are under pressure, when ever-greater demands are being placed on rural women in the face of rapid social transformation and in an increasing number of areas, when evidence of environmental degradation is growing. ICTs are now being used to provide basic solutions to farmers and are increasingly being supported by extension practitioners for use in agricultural production and marketing systems. This paper is intended to investigate the role played by ICT in the dissemination of effective extension information among women livestock farmers in the North West Region of Cameroon. Primary data were collected through the use of structured questionnaires, observations, focus groups and interview guides. Secondary data were obtained from books, journals, research projects and scientific articles. These provided the necessary quantitative and qualitative data that were used to draw conclusions. Multi stage sampling techniques were used to select the female farmers for the study. Four hundred (400) questionnaires were administered randomly to collect data from the communities. The women were examined using different channels like media, phone calls, friends, and agricultural technicians. The results from the study shows that, the media seemingly are doing a great job in disseminating of appropriate agricultural information. Therefore, 40.5% of the farmers get information from the media or magazines but only (7.8%) get information through phone calls. The findings of this study can go a long way to help policy makers in planning and designing need-based information infrastructural system for rural farmers.

The Effect of Cropping Systems on Environmental Impact Associated with Winter Wheat Production—An LCA “Cradle to Farm Gate” Approach

The demand for wheat production is increasing and is associated with environmental effects. To sustain the increased demand, there is a need to find sustainable methods of wheat production. The choice of cropping system can significantly affect the environmental burden of agricultural production systems. This study presents the results of monitoring emission loads resulting from winter wheat cultivation under different cropping systems: organic unfertilized (ORG), organic fertilized (ORG-F), conventional unfertilized (CON), and conventional fertilized (CON-F). The system boundaries include all the processes from “cradle to farm gate” and the functional unit was 1 kg of wheat grain. The primary data were obtained from experimental field trials and secondary data from Ecoinvent v3.5, WFLDB, and Agri-footprint v5.0 databases. The results of this study are related to eight impact categories. The SimaPro 9.2.0.1 software and ReCiPe Midpoint (H) V1.13/Europe Recipe H were used for calculation. The results show that fertilized variants recorded higher environmental impacts compared to the unfertilized variants. The results indicate that ORG-F was more environmentally friendly compared to the CON-F variant at the expense of lower yields. Overall, ORG imposes the lowest environmental impact and is deemed to be more environmentally friendly.

Applications of Remote Sensing as Climate Resilience Technique: A Bibliometric Research Trends

Regional variances in climate, soil, and topography make agricultural production systems particularly fragile. Animal health are negatively impacted by variations in air temperature, precipitation, frequency, and intensity of extreme weather events. For its assessment and administration, cutting-edge methods like remote sensing (RS), global positioning systems, and geographic information systems (GIS) might be very beneficial. The RM & GIS are essential tools with numerous applications for tackling these problems. The impact of climatic and human-induced changes on the environment is receiving more attention as a result of climate change. Due to climate change and human activity, "desertification" describes the degradation of land in arid, semiarid, and dry sub-humid regions. Natural resource sustainability in changing climates can be obtained with the application of RS and GIS. In this review article, the issues towards wildlife were demonstrated and the application of remote sensing was discussed to reduce the impact of climate change to save the wildlife and its preservation. Further, the bibliometric analysis was conducted via R-studio Bibliometric tool, which entailed that developed countries (USA, Canada, Germany) are more forward to applying remote sensing tool to mitigate climate risks.

Automated identification of soil Fungi and Chromista through Convolutional Neural Networks

The identification of soil microorganisms plays a crucial role in agriculture and horticulture, as it enables the monitoring of beneficial species and early detection of pathogens. In this study, we propose a system that utilizes machine vision and machine learning techniques, specifically Convolutional Neural Networks, to automate the identification of different fungi and Chromista based on microscopic images and morphological traits. Our system aims to provide a cost-effective and efficient method for pathogen detection, improving the overall health and productivity of agricultural systems. We conducted experiments using a dataset of soil microorganisms and the performance of the classifier was evaluated using precision, recall and F1-score measures. Despite challenges such as class imbalance and imperfect subimage retrieval, the classifier achieved promising results, with an overall precision of 82% indicating the high accuracy of correctly predicted positive instances across all classes. Furthermore, the incorporation of a majority voting scheme significantly improved the classifier’s performance, addressing the issue of underrepresented classes. The enhanced results demonstrated an average precision and F1-score of 97%. Our work highlights the potential of CNNs in soil microorganism identification and paves the way for future research to expand the dataset and to incorporate a wider range of microorganism genera.

Crop rotation and inoculation increase soil bradyrhizobia population, soybean grain yields, and profitability.

Crop rotation and rhizobial inoculation are strategies to increase yield by means of organic matter addition and modulation of microbial diversity. However, the extent to which these agricultural practices change soil Bradyrhizobium populations, soybean grain yield, and economic benefits to farmers is unclear. Thus, this study aimed to evaluate the interaction between crop rotation and inoculation of soybean (Glycine max) cultivated in two contrasting soils (clayey and sandy soil) on biological nitrogen fixation components, grain yields, and profits. Field experiments with a three-year crop rotation system were carried out to compare effects of inoculation and crop rotations on soil chemical attributes, bradyrhizobia most probable number (MPN) and diversity, soybean nodulation, grain yield, and economic indicators of inoculation in different crop rotations. The crop rotation did not affect the soil MPN cells of bradyrhizobia, but the inoculation and the soil sampling time did, ranging from 3.61-4.42 to 4.40-4.82 in the sandy soil, while in the clayey soil they were from 5.19-6.34 to 6.61-7.14 in Log10 per g of soil with higher population after harvest of summer crops. In the clayey soil, crop rotation influenced soybean nodulation. The grain yield of inoculated soybean in the clayey soil was higher than that in the sandy soil. Soybean inoculation with Bradyrhizobium spp. increased the profitability of agricultural production systems by up to 45% in clayey soil and up to 7% in sandy soil.

Insights into the harvesting tools and equipment's for horticultural crops: From then to now

The evolution of harvesting tools and equipment for horticultural crops has significantly shaped agricultural practices over time. This review paper provides a comprehensive analysis of the journey from historical methods to modern innovations in harvesting technology. The intricate interplay between technological advancements and horticultural practices is explored, highlighting pivotal developments that have revolutionized crop harvesting. Starting with traditional methods employed in the past, we delve into the challenges faced by early horticulturists and the ingenious solutions they devised. The review then transitions to the contemporary landscape, showcasing cutting-edge tools that have emerged through scientific breakthroughs and engineering ingenuity. Noteworthy advancements in machinery design, automation, and precision techniques are discussed, illustrating their profound impact on crop yield, labor efficiency, and overall sustainability.Key insights into the integration of robotics, sensor technologies, and artificial intelligence in modern harvesting equipment are presented, underscoring their potential to further optimize horticultural practices. The paper also addresses the importance of ergonomic considerations and environmental concerns in shaping the future of harvesting tools. Through a comprehensive examination of historical roots and present-day innovations, this review provides a panoramic view of the evolution of harvesting tools and equipment for horticultural crops. By tracing this trajectory, we gain a deeper understanding of how technological progress continues to reshape and enhance the dynamics of crop harvesting, paving the way for more efficient, sustainable, and productive agricultural systems.