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The purpose of this paper is to investigate the application of water harvesting techniques as a sustainability measure of the cropping system for barley production in the Fa'a farming area located in the Northern part of Jordan.

Usually, the farmers plant barley to feed their animals. The climate of the area is semi‐arid to arid. Annual average rainfall in the area is not enough for the survival of barley and farmers are usually grassing barley instead of harvesting. Overgrazing and mismanagement contribute to land degradation in the area, which affect the production system in the area.

The paper investigates the runoff collection system which is framed with two different sizes in three different land uses: cultivated with barley; fallow and rangeland. Data are collected in all of five stormy events. The total soil sediment is measured for these land uses. The amount of water collected from runoff is also measured for the same areas. The ploughing against the slope with planting barley can reduce the runoff and soil sediment increasing soil moisture and reducing soil erosion. The barley production as biomass is highest using strip cropping as opposed to zero ratio control site or conventional cultivation. The plants' lengths were also higher in strip cropping ranging between 26 and 28 centimetres in the different strip cropping ratios compared to 23 centimetres in the conventional cropping system.

By using the results from this new research to such an area, surface runoff from the uncultivated land can be used to supplement the rainfall to the cultivated land. This increases the share of runoff on the cultivated land to the degree where barley can be harvested.

In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.

In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.

In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.

The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Food Industries have to cater a plethora of consumers having variety of tastes. For sustaining in such environment companies create their unique selling point and big data helps them to analyze market situation for such purpose. In this book chapter, the supply chain of fruits and vegetables and the post-harvest losses encountered at each stage in absence of data analytics is discussed. This can be an opportunity for the food industries to reduce food loss and gain better returns on investment by going for a digital transformation. Companies combine big data with technologies like machine learning and artificial intelligence to get faster and more personalized experiences. This chapter includes comparative case studies of food and retail sector for better understanding of the outcome.

In this chapter the development of new sugarcane varieties in Florida and Louisiana is examined, along with the accompanying advancement in mechanization technology through the widespread adoption of sugarcane harvesters. An econometric analysis is carried out to determine the impact of the price of raw sugar on raw-sugar yields in Louisiana and Florida. This study found that in the case of Louisiana, the 3-year lagged US raw-sugar price had a positive and significant impact on sugar yields. The change in raw-sugar prices did not have a significant impact on sugar yields for the Florida industry. Sugar production has increased over time, in part, due to the development of new sugarcane varieties accompanied by modern sugarcane harvesters. Given the relationship between price and yield, particularly in Louisiana, policy makers and producers must be mindful of the potential impact of policy-induced research and development (R&D) on the competitiveness of their industry.

In this chapter, we examine the impact of pay-for-performance incentives on learning-by-doing. We exploit personnel data on fruit pickers paid under two distinct compensation contracts: a standard piece rate plan and one with an extra one-time bonus tied to output. Under the latter, we observe bunching of performance just above the bonus threshold, suggesting workers distort their behavior in response to the discrete bonus. Such bunching behavior increases as workers gain experience. At the same time, the bonus contract induces considerable learning-by-doing for workers throughout the productivity distribution who presumably hope to one day hit the target, and these improvements significantly outweigh the losses to the firm from the bunching. In contrast, under the standard piece rate contract, we find minimal evidence of bunching and only small performance improvements at the bottom of the productivity distribution. Our results suggest that contract design can help foster learning on the job, underscoring the importance of dynamic considerations in principle-agent models.

Agricultural food residues (agro-residues) receive low economic returns and experience disposal problems. The food production and processing is often not configured to supply agro-residues for production of bioenergy needed in food processing. The feasibility of utilising agro-residues through advances in postharvest technology for sustainable bioenergy conversion is reviewed. The paper aims to discuss this issue.

Agro-residues from maize, sugarcane and potatoes in five African countries were assessed from secondary data to identify suitable conversion technologies, energy products and configurations of bioenergy plants for applications in postharvest food processing.

Strategic alignment of postharvest technology to bioenergy production systems is vital to advancing both food production and bioenergy that benefit rural communities in Africa. High economic returns are possible when the bioenergy plants are either annexure to existing agro-processing operations or operate as a biorefinery.

Assessment of energy self-sufficiency of food production and processing systems is required.

Agro-residues for bioenergy production require investments in infrastructure for storage, transportation and processing of the residues, and development of new risk management techniques.

The rural communities will be energy secure resulting in food security through reduced postharvest losses and increased agricultural productivity.

The study stimulates innovative thinking in establishing sustainable bioenergy systems for food processes.

Small farmers, being the primary producers of crops, are the key players in the food supply chain. Yet, they remain the most marginalized in the value chain. The marginalization of small farmers can affect food sustainability. The purpose of this paper is to identify opportunities for bringing more value to small farmers in an agricultural value chain.

This paper makes use of action research, studying the potato value chain, in a developing agricultural country Pakistan. The authors conducted an in-depth study of 37 farmers in four regions, each being a large potato growing ecosystem. The study examined the end-to-end decision-making processes, sources of input (both physical and information), cultivation and sales practices, cost structure, productivity and profitability of the farmers in potato farming.

Large variations exist in the crop yield, cost structure and profitability of farmers within each of and among the four regions due to differences in cultivation practices and approach to sales. There is a significant potential to lower costs, increase yield and enhance overall profitability by using the existing better processes. By addressing the issues faced by small farmers their profits can be potentially doubled. The paper also discusses potential means of recrafting and streamlining the value chain to bring more value to small farmers.

The paper provides a detailed account of how different interventions can increase the value for small farmers. Since the current food supply chain and sustainability are under stress, worldwide, the findings of this study have implications for farmers as well as policy makers.

The literature on streamlining the agricultural value chain and enhancing the share of small farmers is scarce. Improving the value chain and reducing the marginalization of small farmers is an essential step toward increasing food sustainability.

This paper aims to review the dramatic entry of robotics into the field of agriculture.

Research of efforts around the world to apply robots to agricultural needs and participation in webcasts of the latest in agricultural robotic development projects.

Robots are enabling many new and innovative methodologies for performing traditional agricultural tasks of all types. Providing saving in labor, improved performance and faster field coverage.

Readers may be very surprised at the many tasks that robots are already addressing in the agricultural field around the world and the way just applications can have major benefits to farming.

A review of the wide range of the innovative application of robots to agricultural tasks worldwide.

This paper aims to illustrate the growing importance of agricultural robots by providing details of recent product developments and their applications.

Following a short introduction, this first discusses a range of agricultural applications of drones. It then provides details of a selection of mobile field robots and their applications. Finally, concluding comments are drawn.

Commercially available aerial and terrestrial robots are playing a rapidly growing role in a diversity of agricultural practices. Key capabilities and benefits include detecting crop stress and disease, predicting crop yields, reducing agrochemical use, overcoming manpower shortages and reducing labour costs and facilitating precision agricultural practices such as highly localised pesticide and herbicide application and the replacement of large, heavy agricultural machines by fleets of small, lightweight robots.

This provides a detailed insight into the many ways in which robots are transforming agricultural practices.