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.

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.

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.

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.

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.

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.

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.

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.

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.

In the past 20 years, extreme weather events (atypical variations of temperature and precipitation in space and time) have increased in Bosnia and Herzegovina, the consequences being significant material damage and decreased crop yields. The aims of the current paper were to investigate the impact of climate change on yield, irrigation requirements and water productivity of maize grown in one of the most important agricultural regions of the country. It used the results of projections of the EBU-POM regional climate model (Eta Belgrade University – Princeton Ocean Model) for the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B, A2 and A1B > carbon dioxide (CO2), A2 > CO2 for the 2020s (2010–2039), the 2050s (2040–2069) and the 2080s (2070–2099), and it compared them with the reference period (1961–1990) using measured and modelled data. AquaCrop was calibrated for the study area using data from experimental studies. The model was applied in simulations of future cultivation scenarios considering different irrigation regimes and variations in precipitation, temperature, CO2 concentration and sowing date. The results of simulations indicate that in the 2020s, there will be no significant changes in irrigation needs and yield due to the earlier sowing date and overall shifting of the growing season early in spring. A slightly larger negative impact of climate change will occur by the 2050s and a larger one by the 2080s due to a reduction in precipitation in the summer months. Compared with the reference period, in the 2080s, irrigation requirements are expected to increase by almost 100% (from 100 to 200 mm) and to provide up to 30% greater yield. In the future, water productivity of maize will remain high and will be even greater than current levels for both rainfed and irrigated cultivation due to anticipation and shortening of the growing season, reduction in crop evapotranspiration and increase in CO2 concentration. Due to an increase in precipitation in early spring and its reduction in the May–June period, the blue-to-green water ratio will increase in the future with positive environmental connotations. The productive use of blue water will also increase.

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.

Drought represents one of the major constraints on agricultural productivity and food security and in future is destined to spread widely as a consequence of climate change. Research efforts are focused on developing strategies to make crops more resilient and to mitigate the effects of stress on crop production. In this context, the use of root-associated microbial communities and chemical priming strategies able to improve plant tolerance to abiotic stresses, including drought, have attracted increasing attention in recent years. The current review offers an overview of recent research aimed at verifying the role of arbuscular mycorrhizal fungi and chemical agents to improve plant tolerance to drought and to highlight the mechanisms involved in this improvement. Attention will be devoted mainly to current knowledge on the mechanisms involved in water transport.

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.

Knowledge of different fractions and availability of boron (B) is essential while studying the response of crops to B. Fractionation provides information about the chemistry of B and quantifies its bioavailability. Such information is potentially valuable for predicting bioavailability, B leaching, dynamics, transformation between chemical forms in soils and environmental impacts. Total B (T-B) is quantified into five fractions: readily soluble (Rs-B), specifically adsorbed (Spa-B), oxide bound (Ox-B), organically bound (Org-B) and residual B (Res-B). Of these, Rs-B is the fraction present in soil solution and adsorbed weakly by soil particles, and is most readily available for plant uptake. It accounts for 1–2% of T-B. The second most plant available form is Spa-B; it may be adsorbed onto clay surfaces or associated with organic matter (OM) in soil. The remaining fractions, Ox-B, Org-B and Res-B, are unavailable for plant uptake. The major portion (generally 87·4–99·7%) of T-B is composed of Res-B. Overall, the relative proportion of B in various fractions is in the order of Res B > Org-B > Spa-B > Rs-B > Ox-B. Several factors such as soil pH, soil OM, clay minerals, iron and aluminium oxides and calcium carbonate content may change the relative proportion of B in various fractions and the transformations among different soil B fractions. Some of the B fractions are correlated with others and exhibit responses in terms of plant growth. Non-specifically adsorbed (Nsa-B) and Spa-B are positively and significantly correlated to some sub-fractions of Ox-B, such as B occluded in manganese oxyhydroxides (Moh-B). The most readily available forms of B for plants are Nsa-B, Spa-B and Moh-B.

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.

The objective of the present study was to calculate an optimal harvest period for both fresh and ensiled samples of forage maize and to calculate a set of harvest dates (called a harvest window), for which the variety ranking of the fresh forage corresponds with the variety ranking at the optimal harvest period calculated from the ensiled forage. Forage maize is fed almost exclusively as silage, but official variety trials with silage maize determine quality parameters in fresh (i.e. non-preserved) forage. Eight silage maize varieties were monitored at six harvest dates (from 25 to 40% dry matter content) in Merelbeke (Belgium) in 2013–15. At each harvest date, fresh samples were taken and half of the sampled material was ensiled in micro silos for 20 weeks. An optimal harvest period was calculated based on frequently measuring starch concentration and organic matter digestibility for both fresh and ensiled forage. Eventually, harvesting the silage maize at a dry matter content of 32–35% guaranteed an optimal harvest period. Based on the results of eight varieties, reporting variety ranks without going through the ensiling process continues to be a scientifically justified practice in Belgian official variety trials. Varieties with a superior fresh quality keep their leading position after ensiling, but variety differences become smaller after ensiling.