Climate change is a global challenge to ecosystem services, altering crop yields and food security worldwide. In the context of climate change, Onobrychis viciifolia Scop. (sainfoin) can offer a multitude of ecosystem services conferred by its multifaceted beneficial properties. We reviewed the morphological, biochemical and physiological responses to environmental stressors of O. viciifolia, summarized its ecological, agronomic, nutritional and biological interests, and we discussed its use under climate change. Onobrychis viciifolia is a hemicryptophyte forage legume adapted to arid and semiarid regions by evolving a diverse array of protective mechanisms against abiotic stressors at morphological, biochemical and physiological levels. In the present scenario of climate change, O. viciifolia has desirable forage characteristics such as high nutritive value, high voluntary intake and palatability to grazing animals, leading to satisfying animal performance for milk, meat, honey and wool production. Recent studies suggest that O. viciifolia has several highly beneficial phytochemical properties including condensed tannins and polyphenol content, which have been demonstrated to have anthelmintic activities, enhance protein utilization, and prevent bloating. In addition, O. viciifolia also has the potential to reduce greenhouse gas emissions and sequestrate atmospheric carbon and nitrogen into the soil. Ethnobotanical investigations show that O. viciifolia possesses antimicrobial, antiseptic and vulnerary activities. This review could be helpful for understanding of O. viciifolia characteristics, interests and uses, thus promoting its reasonable cultivation under a changing climate.

The increase in demand for dairy products requires continued progress in dairy farming for a sustainable supply. Europe, known as the world's leading milk producer, plays a key role in meeting this growing demand. Modern dairy farming has moved beyond its historical focus on milk yield and now focuses on functional traits such as udder health, fertility and calf survival. As a result, selection indicators have become essential tools, combining multiple attributes to support selective decisions. However, these rates show considerable variability across countries, reflecting their distinct breeding goals. Poland's production and functionality (PF) Index emphasizes production and functional traits to enhance dairy cattle. Portugal uses the total economic merit (M€T) and total performance index (IPT) for a broader assessment covering a wider range of traits. Ukraine is transitioning towards a more comprehensive breeding system incorporating stress tolerance and longevity. Factors such as climate change and the need for sustainable practices drive this evolution, underscoring the economic importance of traits beyond mere production. Future trends may include features such as feed efficiency, methane emissions reduction and stress resistance. Diverse breeding objectives across countries lead to different selection index constructions, essential for effective selection, ranking and breeding of superior individuals. This comprehensive review offers insight into constantly evolving dairy farming strategies in Europe, with a focus on Poland, Portugal and Ukraine, while highlighting the key role of functional traits in shaping the future of dairy farming.

Australia has a diverse and unique native flora with thousands of edible plant taxa, many of which are wild relatives of important food crops. These have the potential to diversify and improve the sustainability of Australian farming systems. However, the current level of domestication and cultivation of Australian plants as food crops is extremely limited by global standards. This review examines the current status and potential for future de novo domestication and large-scale cultivation of Australian plants as food crops. This is done in the context of international new crop development and factors that impact the success or failure of such efforts. Our review finds considerable potential for native Australian plants to be developed as food crops, but the industry faces several significant challenges. The current industry focuses on niche food markets that are susceptible to oversupply. It also suffers from inconsistent quantity and quality of product, which is attributed to a reliance on wild harvesting and the cultivation of unimproved germplasm. More active cultivation is necessary for industry growth, but attempts have historically failed due to poorly adapted germplasm and a lack of agronomic information. The de novo domestication and large-scale cultivation of Australian plants as food crops will require an investment in publicly supported multidisciplinary research and development programmes. Research programmes must prioritize the exploration of plants throughout Australia and the collection and evaluation of germplasm. Programmes must also seek to engage relevant stakeholders, pursue participatory research models and provide appropriate engagement and benefit-sharing opportunities with Indigenous Australian communities.

Lodging is the permanent displacement of stalks due to disrupted secondary cell walls caused by external factors, plant characters and their interaction. Anatomical, morphological and compositional traits are among lodging-inducing plant traits. In comparison with morphological and anatomical features, the correlation of lodging resistance and cell wall composition is not frequently reviewed. In this review, the relation between cell wall composition and lodging resistance of cereal stalks is comprehensively reviewed based on major cell wall components (lignin, cellulose and hemicellulose) and trace minerals. From the body of literatures reviewed across all cereal crops, lignin and cellulose were found to have significant positive correlation with lodging resistance. However, the effect of structural features of cellulose and lignin on lodging resistance was not investigated in most of the studies. This review also highlights the importance of biomass recalcitrance and lodging resistance trade-offs in the spectrum of genetic cell wall modifications.

Arazyme, an alkaline metalloprotease, is produced by Serratia proteamaculans, a symbiotic bacterium isolated from the intestinal ecosystem of Nephila clavata. Arazyme is known to play a crucial role in facilitating the digestion process in N. clavata. Recently, there has been increasing interest in exploring invertebrate-associated gut symbionts as a valuable source of novel and biologically active enzymes. Animal husbandry has shown significant interest in this spider-derived bioactive enzyme. This paper aims to provide a comprehensive review of the current understanding and knowledge of arazyme in the context of animal husbandry, offering valuable references for potential applications of this enzyme.

The current study aimed to investigate biofortification of maize grown under different irrigation intervals, i.e. 15, 20 and 25 days (hereinafter referred to as IR15, IR20 and IR25, respectively), using foliar application treatments (silicon (Si), zinc (Zn), silver nanoparticles (AgNPs), Si + Zn, Si + AgNPs, Zn + AgNPs and Si + Zn + AgNPs) in two growing seasons, 2020 and 2021. A split-plot design with four replications was used, where irrigation intervals and foliar treatments were assigned in main plots and subplots, respectively. IR15 received a total of 7925 m3/ha irrigation water divided over seven irrigations, while IR20 received 5690 m3/ha divided over five irrigations and IR25 received 4564 m3/ha divided over four irrigations. The highest yield and grain quality were observed in plants irrigated at 15-day intervals. Spraying the canopy with Si, Zn and AgNPs, either individually or in combination, reduced the negative impact of water stress caused by longer irrigation intervals on plant growth, yield, yield components and grain protein content. In IR15 + AgNPs + Zn, most of the studied parameters, except for proline content, showed a high positive impact, especially on 100-kernel weight (KW). In contrast, IR25 + Si + AgNPs + Zn showed the highest positive effects on proline and protein contents but a negative impact on the harvest index. Collectively, IR15 + Si + AgNPs + Zn resulted in the highest values of all studied parameters, followed by IR15 + Si + AgNPs and IR15 + Si + Zn. In conclusion, our results suggest that an irrigation interval of 15 days combined with application of Si, Zn and AgNPs has the potential to improve yield and quality of maize under water deficit stress.

The sheep farming has economic and sociocultural importance in semi-arid regions worldwide. Of the total of 1.2 billion sheep in the world, 0.4 are found in semi-arid regions. In this review, we have discussed the main risk factors for neonatal lamb mortality and its association with the prevailing environmental conditions of tropical semi-arid regions. Over the last decades, the average mortality rate of newborn lambs remained relatively constant (~15%) around the world. This rate is reported to be higher (Up to 30%) in small-scale sheep farming systems from developing countries. Overall, the main risk factors of neonatal mortality include low birth weight, dystocia, adverse weather conditions, inadequate milk, or colostrum supply, competition between siblings in multiple births, and poor expression of maternal behaviour. In tropical semi-arid regions, recent findings revealed that newborn lambs from hair coat sheep breeds are less vigorous to perform the first suckling when even exposed to moderate cold conditions (18–20°C). On the other hand, the high radiant heat load in these areas can also challenge the thermoregulation of ewes and newborn lambs, especially if they are kept in areas without protection against direct short-wave solar radiation. Under such circumstances, newborn lambs were reported to be hyperthermic. The heat stress as risk factor for neonatal lamb mortality is a topic that deserves more investigation, particularly in tropical semi-arid areas, where is expected to be drier and hotter as consequence of rapid advances in climate change.

Small-grain farming systems in Mediterranean climatic regions are characterized by poor quality soils, high climate variability, and resulting heavy agrochemical reliance. The adoption of Conservation Agriculture, based on minimum tillage, permanent soil cover, and crop rotations, has improved soil quality, enhanced crop productivity, and help mitigate financial risk. However, emerging issues that threaten sustainability, such as herbicide resistance, inputs costs rising disproportionately to product prices, and increasing climate variability and unpredictability, drive the need for innovation in small-grain Mediterranean-based CA systems. The aim of the review was to evaluate a set of agroecological practices which constitute a Regenerative Agriculture (RA) concept, for their potential to address these challenges from a soil quality, crop productivity and whole-farm economics perspective. Organic soil amendments derived from agro-wastes, offer promising perspectives for supplying appropriate quantities of nutrients to reduce or replace mineral fertilisers and offset their economic and agroecological costs. Although the viability of microbial bioeffectors in small-grain agroecosystems was largely under question, non-microbial bioeffectors and certain combination options represent more efficient and cost-effective uses of this technology. Their widely reported abiotic stress priming functions and crop productivity enhancement under poor growing conditions may improve yield stability and financial resilience in small-grain Mediterranean CA systems. Finally, multi-species cover crops subjected to adaptive multi-paddock (AMP) grazing as a phase within the crop rotation may reduce weed pressure, enhance soil multi-functionality, and resilience against environmental stresses. To validate the potential of these technologies in Mediterranean small-grain systems, more long-term and context-specific research is called for.

Bambara groundnut (Vigna subterranea L. Verdc; BGN) is an important legume grown mainly by small-scale subsistence farmers in sub-Saharan Africa, in parts of Thailand and Indonesia. It has a high concentration of seed carbohydrate (55–70%), protein (17–25%), fat (1.4–12%) and dietary fibre (5.2–6.4%). A range of biotic and abiotic stresses together with socio-economic constraints affect its productivity, yield and quality. The changing climate and a growing world population are putting pressure on food production, as world food supply is heavily reliant on few crops. As such, there is a need to broaden crop usage and increase yield of minor crops such as BGN. Improvements in production can potentially be achieved by a combination of advanced phenotyping, genotyping, environmental characterization and overall management approaches. Breeding for advanced lines is complicated by overreliance on landraces. This review aims to provide the current status of BGN production, production constraints and approaches to overcome these, as well as its grain composition and nutritional value. It further discusses and elaborates on potentially available opportunities for overall improvement, so that BGN, like all neglected crops, can play a valuable role in world food security. Efforts should be intensified to improve the overall utilization of BGN and its constituents to make it an economically viable crop.

Integrated pest management (IPM) seeks to minimize the environmental impact of pesticide application, and reduce risks to human and animal health. IPM is based on two important aspects – prevention and monitoring of diseases and insect pests – which today are being assisted by sensing and artificial-intelligence (AI) techniques. In this paper, we surveyed the detection and diagnosis, with AI, of diseases and insect pests, in cotton, which have been published between 2014 and 2021. This research is a systematic literature review. The results show that AI techniques were employed – mainly – in the context of (i) classification, (ii) image segmentation and (iii) feature extraction. The most used algorithms, in classification, were support vector machines, fuzzy inference, back-propagation neural-networks and recently, convolutional neural networks; in image segmentation, k-means was the most used; and, in feature extraction, histogram of oriented gradients, partial least-square regression, discrete wavelet transform and enhanced particle-swarm optimization were equally used. The most used sensing techniques were cameras, and field sensors such as temperature and humidity sensors. The most investigated insect pest was the whitefly, and the disease was root rot. Finally, this paper presents future works related to the use of AI and sensing techniques, to manage diseases and insect pests, in cotton; for instance, implement diagnostic, predictive and prescriptive models to know when and where the diseases and insect pests will attack and make strategies to control them.

Banana is one of the main fruit crops in the world as it is a rich source of nutrients and has recently become popular for its fibre, particularly as a raw material in many industries. Mathematical models are crucial for strategic and forecasting applications; however, models related to the banana crop are less common, and reviews on previous modelling efforts are scarce, emphasizing the need for evidence-based studies on this topic. Therefore, we reviewed 75 full-text articles published between 1985 and 2021 for information on mathematical models related to banana growth and, fruit and fibre yield. We analysed results in order to provide a descriptive synthesis of selected studies. According to the co-occurrence analysis, most studies were conducted on the mathematical modelling of banana fruit production. Modellers often used multiple linear regression models to estimate banana plant growth and fruit yield. Existing models incorporate a range of predictor variables, growth conditions, varieties, modelling approaches and evaluation methods, which limits comparative evaluation and selection of the best model. However, the banana process-based simulation model ‘SIMBA’ and artificial neural network have proven their robust applicability to estimate banana plant growth. This review shows that there is insufficient information on mathematical models related to banana fibre yield. This review could aid stakeholders in identifying the strengths and limitations of existing models, as well as providing insight on how to build novel and reliable banana crop-related mathematical models.

Livestock plays a crucial role in food and nutrition security. However, livestock production accounts for 0.18 of global greenhouse gas emissions. India has one of the highest livestock densities globally, mainly produced under traditional systems. Specifically, the emission and particularly nitrogen losses from cattle in traditional systems cannot be ignored. Nitrogen emission is substantial when cattle roam free and waste is not collected or managed efficiently. This paper reviews the literature to piece together the available information on nitrogen emissions from cattle in India to synthesize the evidence, identify gaps and contribute to further understanding of the problem. At the same time, the paper highlights the solutions to reduce nitrogen pollution from cattle production in India. The main findings are that most cattle in India are not reared to provide meat protein. The implication is that reactive nitrogen per capita consumption is lower than most developed countries. However, there are substantial inefficiencies in feed conversion, feed nitrogen use and manure management in India. As a result, nitrogen losses and wastage are considerable in the different production systems. Furthermore, the review suggests that social, cultural and economic factors such as convergent social behaviour, urbanization, regulations, changing consumption patterns, the demand for cheap fuel sources, culture and religion influence the production systems and, consequently, the emissions from livestock. Suggested solutions to reduce nitrogen pollution from cattle production in India are improving livestock productivity, adopting better feeding, manure and pasture management practices and using behavioural nudges.

Weeds are a major biotic constraint to the production of crops. Studies on the critical period of weed control (CPWC) consider the yield loss due to the presence of all weeds present in the crop cycle. The CPWC is the time interval between the critical timing of weed removal (CTWR) and the critical weed-free period (CWFP), and the weed presence before and after the extremes of CTWR and CWFP may not significantly reduce crop yield. The crop yield is taken into consideration and weed density or biomass of individual weeds (annual or perennial) is not so important while calculating the CPWC. Only weed density or biomass is considered for calculating weed control efficiency of a particular management practice for which the weed seed bank is also a criterion. However, weed biomass is the outcome after competition experienced by each weed species with the fellow crop and the weeds. Consequently, the weed pressure in the subsequent season will be the cumulative effect of the preceding season too, which is unaccounted for in CPWC. It is argued that in organic farming or low-input farming systems, where herbicides are not used, the concept of CPWC can be misleading and should be avoided. It is concluded that CTWR is more meaningful than the CPWC.

Modern agriculture needs proper solutions to face the current trend of pesticides and fertilizers reduction. One of the available leverages to support this transition is the use of bioproducts that are more environmentally friendly and less hazardous for human health. Among them, blue biotechnology and more precisely seaweed and microalgae gain interest every year in the scientific community. In agriculture, seaweeds (Macroalgae) have been used in the production of plant biostimulants while microalgae still remain unexploited. Microalgae are widely described as renewable sources of biofuels, bioingredients and biologically active compounds, such as polyunsaturated fatty acids (PUFAs), carotenoids, phycobiliproteins, sterols, vitamins and polysaccharides, which attract considerable interest in both scientific and industrial communities. They affect agricultural crops for enhancement of plant growth, seedling growth. They can also improve nutrient incorporation, fruit setting, resistance properties against pests and diseases, improving stress management (drought, salinity and temperature). The present review aimed at the interest of blue biotechnology in agronomy, with a specific focus on microalgae, their biological activities and their possible application in agriculture as a potentially sustainable alternative for enhanced crop performance, nutrient uptake and resilience to environmental stress. This review does not only present a comprehensive study of microalgae as plant biostimulants but also as biofertilizers, with a particular emphasis on future challenges these solutions will have to deal with, microalgae being able to synthesize secondary metabolites with potential biopesticidal action.

Participatory plant breeding (PPB), commonly applied in the Global South to address the needs of underserved farmers, refers to the active collaboration between researchers, farmers and other actors throughout the breeding process. In spite of significant public and private investments in crop variety improvement in the Global North, PPB is increasingly utilized as an approach to address cropping system needs. The current study conducted a state-of-the-art review, including a comprehensive inventory of projects and five case studies, to explore the emergence of PPB in the Global North and inform future PPB efforts. Case studies included maize (Zea mays), tomato (Solanum lycopersicum), Brassica crops (Brassica oleracea), wheat (Triticum aestivum) and potato (Solanum tuberosum). The review identified 47 projects across the United States, Canada and Europe including 22 crop species representing diverse crop biology. Improved adaptation to organic farming systems and addressing principles and values of organic agriculture emerged as consistent themes. While projects presented evidence that PPB has expanded crop diversity and farmer's access to improved varieties, obstacles to PPB also emerged including challenges in sustained funding as well as addressing regulatory barriers to the commercial distribution of PPB varieties. Agronomic improvements were only one lens motivating PPB, with many projects identifying goals of conservation of crop genetic diversity, farmers' seed sovereignty and avoidance of certain breeding techniques. The authors conclude that a multidisciplinary approach is needed to fully understand the social, political and agroecological influences driving the emergence of projects in the Global North and factors impacting success.

Crop production is at the core of a ‘perfect storm’ encompassing the grand challenges of achieving food and nutrition security for all, in the face of climate change, while avoiding further conversion of natural habitats for agriculture and loss of biodiversity. Here, we explore current trends in crop modelling related to these grand challenges by reflecting on research presented at the Second International Crop Modelling Symposium (iCropM2020). A keyword search in the book of abstracts of the symposium revealed a strong focus on ‘climate change’, ‘adaptation’ and ‘impact assessment’ and much less on ‘food security’ or ‘policy’. Most research focused on field-level investigations and far fewer on farm(ing) systems levels – the levels at which management decisions are made by farmers. Experimentation is key to development and testing of crop models, yet the term ‘simulation’ outweighed by far the terms ‘experiments’ and ‘trials’, and few contributions dealt with model improvement. Cereals are intensively researched, whereas roots, tubers and tropical perennials are under-researched. Little attention is paid to nutrient limitations apart from nitrogen or to pests and diseases. The aforementioned aspects represent opportunities for future research where crop models can help in devising hypotheses and driving new experimentation. We must also ensure that crop models are fit for their intended purposes, especially if they are to provide advice to policymakers. The latter, together with cross-scale and interdisciplinary efforts with direct engagement of stakeholders are needed to address the grand challenges faced by food and agricultural systems in the next century.

It is challenging to predict the changes in weed flora that may occur because of changes in global climate. Limited data are available on the effect of climate change and drought conditions on weed flora and their competitiveness in Southern Europe. Future predictions by scientists indicate reduced and untimely rainfall, along with increased temperatures in this region. Weeds possess a variety of developmental and physiological mechanisms, including senescing, increased leaf cuticular wax deposition, well-developed palisade parenchyma in the leaves, high root/shoot ratio, stomatal closure, peroxidase accumulation and symbiosis with endophytes that enable them to adapt to drought and high temperatures. Because of high adaptability of weeds to adverse environmental conditions, it can be assumed that under future warmer and drier environmental conditions, their growth will be favoured, while the competitiveness of vegetable crops against weeds will be decreased. It is important to highlight that the predicted decrease in overall rainfall levels throughout the year may lead to increased problems of herbicide residues (carryover effects) to following crops. The current paper provides an up-to-date overview of the adaptation mechanisms of weed species commonly found in Southern Europe, in order to expand the available knowledge regarding their response to drought and elevated temperatures. Emphasis is placed on revealing the effects of drought and increased temperatures on vegetable–weed competition and, most importantly, its effect on vegetable crop yield.

Hailed as the single most important paper published on crop protection in the 20th century, Stern et al. in 1959 formed the conceptual basis for modern integrated pest management (IPM) worldwide. The ecological foundation for IPM envisioned by its authors is as valid today as in 1959. However, adoption by developing country farmers has been low and its advances short-lived. The present paper examines the concept of integration in IPM and criteria for determining whether its control tactics have been integrated harmoniously. The effects of local and regional landscape patterns on pests and on the design of IPM are reviewed, arguing that the agroecosystem must be understood and managed as a living system with the goal of enhancing and conserving agrobiodiversity and keeping ecosystem services intact. Key to IPM adoption is convincing farmers to integrate non-chemical alternatives (e.g. biological control, plant diversification) as primary management components and to apply pesticides judiciously and only after non-chemical components fail to manage pests effectively. Research, extension and policy changes are identified to increase the efficiency, adoption and sustainability of IPM on resource-limited farms. The over-arching challenge is devising communication and support systems that allow resource-limited farmers to try, adopt and sustain IPM that enhances yields and profits in light of the many uncertainties and challenges. Use of information technology, media development, crowdsourcing and rural sociology is advocated to connect farmers to the technical sources required to enhance yields and profits and reduce risks to them, the farming community and the environment.

Deep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.