Potential Crop Research Articles

Diversity of bees during flowering of onion crops in a xeric envi-ronment

The study of bee species present in agricultural landscapes is necessary to identify potential crop pollina-tors and promote the ecosystem services they provide. Bee community studies are the first step to know the bee species in a given area, as well as their abundance and activity periods. The aim of this research was to identify bee species as potential pollinators of onion crops in a xeric area of Argentina (San Juan province). Specimens were captured with elevated pan traps during flowering of onion crops, and the bee community was characterized by functional traits such as body size, nesting biology, and sociality. Additionally, we stud-ied the period of activity for each species in relation to the peak of onion blossom. Traps collected a total of 1,765 individuals and 55 species/morphospecies of bees were identified, reporting 21 new records for San Juan province. The most abundant species were Apis mellifera L. and two species of Lasioglossum (Dialic-tus) Robertson. The bee community was characterized by a great richness of species of small and very small size, with a preference for nesting below ground and solitary behavior. Among wild bees, genus La-sioglossum Curtis showed a high diversity (richness and abundance) during onion flowering. Future studies are needed to determine their contribution to the pollination of this crop. In this study, we highlight the diver-sity of wild bees present during onion flowering in a xeric environment and its importance for future ecolog-ical, conservation, and pollination studies.

Eco-physiological response of grain amaranth to regulated deficit and conventional deficit irrigation applied with surface and subsurface drip irrigation systems

Abstract Understanding the water use of drought-tolerant crops of the drought-prone Mediterranean regions is important for sustainable agriculture. The aim of this study was to evaluate the yield and yield responses of amaranth (Amaranthus hybridus L.) to different irrigation strategies conducted in 2019 and 2020 under Mediterranean climatic conditions using surface drip (SD) and subsurface drip (SSD) systems. Strategies investigated were: regulated deficit irrigation (RDI), conventional deficit irrigation (DI25, DI50, DI75), full irrigation (FI) and rainfed treatment. The highest grain yield was observed in FI treatments; RDI treatments produced 5% lower grain yield than the FI treatments, although the RDI treatments resulted in water savings of 23 and 21% for SD and SSD systems, respectively. DI treatments resulted in lower leaf water potential (LWP) and higher crop water-stress index (CWSI) compared to FI in both systems values. The results showed that optimum irrigation conditions to obtain the highest amaranth grain yields were associated with an LWP of −1.0 MPa and an average CWSI of about 0.25. The FI treatments under SSD systems had the highest grain production, followed by FI under SD and RDI under both the drip systems. Under SD and SSD systems, RDI saved 23 and 21% water, respectively, and produced a yield statistically comparable to that of FI. The SSD methods generated higher net income than SD. From these results it can be concluded that both RDI and DI75 could be a good alternative to FI under the conditions of water scarcity in the Mediterranean region.

Cut-off walls alter nitrogen loads and fluxes in small islands

The measure of constructing cut-off walls in coastal islands has been tested effective to upgrade the freshwater lenses (FWL). Meanwhile, cut-off walls can also affect the FWL quality by changing the loads and fluxes of nitrogen (N). Previous research has been focused on the effects of cut-off walls on the N fluxes in groundwater (like, e.g., nearshore denitrification), assuming a constant N source while changes of the N loads and fluxes associated to the N source in shallow soils have only rarely been considered. In this study, we numerically simulated seawater intrusion and transport of land-sourced nitrate on a 2D, vertically oriented island equipped with a cut-off wall. A simplified framework was used to simulate the N fate in the shallow soil. We selected different aquifer permeability and the depth of cut-off wall. The cut-off wall effect on the N loads and fluxes in soil and groundwater, and on the nitrate concentration within the FWL, was quantitatively assessed. The results show that cut-off walls can considerably reduce the soil N load in high permeability islands (> 10−11.5 m2), because of the increased potential crop uptake due to the increased groundwater level and higher soil moisture. The opposite effect was observed in low permeability islands (< 10−11.5 m2) due to the smaller N leaching fluxes from soil to groundwater under the reduced groundwater flow rate. For the high and low permeability islands the leaching flux can generally be reduced by up to 63 %; for the intermediate permeability islands (10-11.5 m2 ≤ k ≤ 10-11 m2), however, the leaching is increased by up to 38 %. Highly depending on how the leaching changes, cut-off walls can reduce the groundwater N load and the nitrate concentration of FWLs, and thus improve the FWL quality in many cases except for the intermediate permeability islands. Therefore, special caution and investigation are required for constructing cut-off walls on islands with intermediate permeability. As a first attempt to assess the influence of cut-off walls on N loads and fluxes considering the N source dynamics, our study is important for the management of FWL quality and coastal environments.

A spatial assessment of agricultural vulnerability to climate change using multidimensional data in Maharashtra state of India

This study aims to examine district-level agricultural vulnerability in the semi-arid and drought-prone state of Maharashtra. Using a multidimensional approach, suitable indicators for 34 districts were selected under exposure, sensitivity and adaptive capacity, aligning with the IPCC 2007 framework for vulnerability assessment. The analysis used temperature and rainfall data spanning 50 years (1968–2017). It also incorporated area and yield data from 2011 to 2020, along with other indicators for the year 2019–20. The composite agricultural vulnerability index (CAVI) score was obtained using proper normalization and assigning weights. The study revealed that the Marathwada and Vidarbha regions reported higher potential crop losses. Among the districts, Sangali was the most exposed (0.57), while Jalgaon was the least exposed (0.31) to climate variations. Ahmednagar exhibited the highest sensitivity (0.70), while Nagpur showed the lowest sensitivity (0.34) to climate change. Regarding adaptive capacity, Pune demonstrated the highest resilience (0.57) and Sindhudurg displayed the lowest score (0.23). CAVI highlighted that the Konkan region, along with western Maharashtra and eastern parts of Vidarbha, exhibited relatively lower vulnerability to climate change, due to factors such as lower potential crop loss and better adaptive capacity. In contrast, Marathwada and western Vidarbha regions were more vulnerable, facing significant crop production losses and higher exposure to climate variability. Hence, to build resilience to climate change and reduce crop production losses, crop planning needs to be adjusted according to the climatic conditions in these regions.

Spatial heterogeneity analysis on distribution patterns and underlying determinants of historical rural settlements: a case study in Taihang Baxing

As vital carriers of cultural heritage, rural areas are facing the threats of degradation and urbanization during rapid urbanization processes. In recent years, China has formulated and implemented a series of national policies, regulations, and heritage protection lists to safeguard and perpetuate the traditional patterns and historical features of rural heritage. However, these rural heritage listings exhibit significant macro-level distributional imbalances and generally lack consideration of regional characteristics. This study uses the Taihang Baxing (THBX) in China as a case study, initially employing historical local documents and maps as base data. It utilizes the stability in the evolution of Chinese characters to compare historical and contemporary village names, filtering out existing historical rural settlements (HRS) to construct a dataset. Further, a multi-scale geographically weighted regression (MGWR) model is applied to analysis the impacts of natural, economic, and social factors on the concentration of HRS (CHRS). The study reveals that the CHRS in the region is markedly characterized by clustering, with evident spatial heterogeneity in the impacts of various factors. Slope (SLP), total annual precipitation (TP), and potential crop yield (PCY) significantly influence the CHRS globally, while elevation (ELV), distance to ancient path (DAP) and surface runoff (DSR), gross domestic product (GDP), and road density (RD) have significant local impacts. This research offers a methodological framework for HRS data compilation and provides theoretical and methodological references for national and local levels of rural heritage protection, optimization of rural spatial patterns, prediction of rural evolution pathways, and implementation of China’s rural revitalization strategy.

Improving crop production using an agro-deep learning framework in precision agriculture

BackgroundThe study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity.ResultsThe study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses.ConclusionsThe ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments.

African Horned Melon (Cucumis metuliferus): Climate‐Resilient Crop and Gene Donor in the Breeding of Major Cucurbits

ABSTRACTAfrican horned melon (Cucumis metuliferus E. Meyer ex Naudin, 2n = 2x = 24) is an under‐researched and under‐utilised cucurbit crop primarily grown for its nutritious fruit. In its centre of diversity, the crop is valued for its relatively high tolerance to insect pests, diseases, drought and heat stress. It is a potential opportunity crop and a valuable source of genes to major Cucumis species, including cucumber (Cucumis sativas L.) and melon (C. melo L.). Its climate resilience and nutrient‐rich fruit provide niche market opportunities. Hence, production and value‐adding will make African horned melon a crop of choice globally. There is a need for an in‐depth investigation into the genetic diversity, breeding and food composition of the crop. Therefore, the objective of this review was to provide perspectives on the production and breeding status of African horned melon to appraise its genetic value for human welfare, strategic production, genetic conservation and breeding of market‐preferred varieties, including closely related Cucumis species. The first section described the botany, production status, germplasm resources and characterisation based on phenotypic and genetic markers. This is followed by breeding progress for biotic and abiotic stress tolerance, utilities and challenges of gene transfer and potential rootstock to Cucumis species, especially cucumber and melon. The review summarised the main breeding goals and approaches, including mutation breeding to fast‐track the development of new varieties. Information presented in the review will guide cultivar design in African horned melon or related cucurbits, aiming for superior agronomic and nutritional quality traits and tolerance to biotic and abiotic stresses.

Targeted modulation of pennycress lipid droplet proteins impacts droplet morphology and seed oil content.

Lipid droplets (LDs) are unusual organelles that have a phospholipid monolayer surface and a hydrophobic matrix. In oilseeds, this matrix is nearly always composed of triacylglycerols (TGs) for efficient storage of carbon and energy. Various proteins play a role in their assembly, stability and turnover, and even though the major structural oleosin proteins in seed LDs have been known for decades, the factors influencing LD formation and dynamics are still being uncovered mostly in the "model oilseed" Arabidopsis. Here we identified several key LD biogenesis proteins in the seeds of pennycress, a potential biofuel crop, that were correlated previously with seed oil content and characterized here for their participation in LD formation in transient expression assays and stable transgenics. One pennycress protein, the lipid droplet associated protein-interacting protein (LDIP), was able to functionally complement the Arabidopsis ldip mutant, emphasizing the close conservation of lipid storage among these two Brassicas. Moreover, loss-of-function ldip mutants in pennycress exhibited increased seed oil content without compromising plant growth, raising the possibility that LDIP or other LD biogenesis factors may be suitable targets for improving yields in oilseed crops more broadly.

Assessing the economic and nutritional value of pollination services in Nepal

Pollination is a key ecosystem service crucial for supporting agricultural production, economic growth, social inclusion, and environmental protection. Understanding the economic value of pollination and its impact on human health and nutrition is essential for effective pollinator conservation and management. This study evaluates the economic and nutritional value of pollination services in Nepal and quantifies historic changes in pollinator reliance. Using public data on agricultural production and commodity prices, in combination with published nutritional composition values, we employ the dependency-ratio method to quantify economic and nutritional value across different regions of the country and through time. We conservatively estimate the annual economic value of pollination services in Nepal at US $477 million, representing 9% of total agricultural revenue. Pollinator-dependent crops, particularly fruits and vegetables are the source of essential nutrients; 40% of plant-based vitamin A and 14% of vitamin C are directly attributable to insect pollination. The cultivated area of these pollinator-dependent crops has increased by 91% in Nepal over 20 years – 3.7 times faster than equivalent increases in non-pollinator-dependent crops. The decline in wild pollinators during the same time period poses a threat, leading to potential pollination deficits and crop losses. Our study underscores the importance of conserving and managing pollinators to ensure sustainable agriculture, food security, and nutrition. Targeted efforts, including policy interventions and conservation strategies, are needed to safeguard pollinator populations and enhance pollination services.

Impacts of adaptation to climate change on farmers’ food security and level of sesame production in Western Ethiopia

The existence of multiple stresses and poor adaptive capacity make Africa most susceptible to climate change. In Ethiopia the potential adverse effects of climate change on the agricultural sector, the main stay of the country’s economy, are major concerns. To ensure food security, reducing the vulnerability of agricultural systems through different feasible adaptation strategies is one of the policy options in response to climate change impact This study is a first of its kind in examining the relative effectiveness of various adaptation strategies in ensuring farmers’ food security and enhancing level of sesame production in rural area of Western Ethiopia. In addition to data obtained from meteorological stations, cross-sectional data were collected interviewing 400 farm households. Descriptive statistics, two-stage least square (2SLS) and double-hurdle (D-H) models were used to analyze the data. The results of the study indicate that households are adapting using various strategies to the looming climate change in the area. The study also indicated that, though sesame production was negatively impacted by the climate hazards, smallholders have continued its production at minimum level due mainly to its high value crop character. 2SLS estimation results revealed that rainfall and temperature variability have negative impact on household’s food security. Moreover, the result indicates effectiveness of climate adaptation strategies namely agronomic practices, irrigation and soil and water conservation in reducing climatic risks and ensuring household food security. The result also implicitly indicated that farmers continued to adapt sesame production under risk climate and it is contributing to farmers’ food security. Further, the result revealed that climate change adaptation strategies have positively impacted the level of sesame production. Consequently, policy that augments households’ climate awareness and promotes adaptation decision and strategies could help reducing risks pertaining to climate and thereby improves farmers’ food security status and production of high value export potential crop—sesame.

The potential footprint of alternative meat adoption on corn and soybean producers

Alternative meat (AltMeat), which includes plant-based and cultured meats, has the potential to improve the global food supply chain on numerous environmental and societal dimensions. Some of these gains, derived from lower animal meat consumption, could disrupt the supply chain of crops that are used as animal feed. This study evaluates the potential impact of animal meat displacement on major corn and soybean-producing regions, globally or in developed regions. We combine trading patterns and regional cost structures with potential crop demand reductions caused by animal meat displacement, to occur by 2032 on top of an Organisation for Economic Co-operation and Development and the Food and Agriculture Organization of the United Nations (OECD-FAO) baseline projection without AltMeat. We find that potential animal meat displacement in the US and the European Union (EU) would have a minor effect on crop markets. Worldwide displacement, however, would lead to significantly lower corn and soybean prices relative to the baseline OECD-FAO projection without AltMeat. We explore quantitatively the heterogeneous impact of such developments in crop-producing regions. Our findings shed light on a trade-off associated with the significant benefits of more sustainable meat production.

Outlook on nutritional importance and breeding strategies for oil and quality improvement in sesame (Sesamum indicum L.)

Sesame (Sesamum indicum L.), is a pivotal crop in global agriculture due to its high oil content (50-60 %), rich in quality unsaturated fatty acids and an array of bioactive compounds like sesamin and sesamol. This review is a compilation of information on sesame nutrition, mechanism of action of bioactive principles, health benefits in terms of unsaturated fats, antioxidants and essential nutrients to combat health disorders. The genetic resources are the base for any crop improvement programs. The genetic wealth of this crop as ex-situ conservation world-wide, traits of importance from its wild relatives, studies on genetic diversity through agronomic and molecular techniques for quality traits are focused. Both conventional and molecular breeding methods are examined. Conventional breeding efforts covers trait variability, correlations, gene action, combining ability studies, breeding methods and varieties released. The insights into molecular biology probes the novel QTLs, discovery of candidate genes, molecular markers for quality traits, genome sequencing, advancements in gene expression studies, genetic maps and pangemone. Despite the significant progress, the lacuna and future prospects in these areas are highlighted. Integrating the information from modern genomic technologies with conventional methods is the key to maximize the crop's potential for oil and nutritional security.

Physicochemical characteristics and nutritional value of safflower oil: A potential sustainable crop for Egypt

Background: The Increasing demand for sustainable and economical non-traditional edible oils, as alternatives to common oils is pivotal to bridge the edible oils gap, coupled with negative impacts of climate change on the agroecological settings for common oilseed crop productivity. Safflower, being one of the fast-growing medicinal oilseeds crops rich in polyunsaturated fatty acids, known as the “king of linoleic acid”, exhibits resilience to adverse environmental conditions such as high temperatures, drought, salinity, and marginal environments. Aims: This study aimed to comprehensively assess the physicochemical characteristics and lipid nutritional indices of safflower oil to validate its potential for expanded cultivation in Egypt. Materials and Methods: Safflower oil was extracted from seeds of two spineless varieties cultivated in Egypt. The oil was subjected to proximate analysis, physicochemical characterization, fatty acid profile determination, and α-tocopherol content analysis. Additionally, a frying stability test was carried out for safflower oil and its blends with soybean oil in different ratios, monitoring changes in free fatty acid, peroxide value, and total polar compounds. Lipid nutritional indices were calculated to assess the oil’s health-promoting properties. Results: Safflower oil exhibited similar proximate composition and physicochemical characteristics to sunflower oil. The fatty acid profile of safflower oil was comparable to sunflower oil, with a lower oleic acid content and a higher linoleic acid content. Furthermore, safflower oil demonstrated satisfactory stability during the frying process. Lipid nutritional indices calculated based on the fatty acid profile revealed that safflower oil is a valuable source of ω-6 fatty acids. The oil exhibited favorable values for atherogenicity index (AI), thrombogenicity index (TI), hypocholesterolemic / hypercholesterolemic (HH), health-promoting index (HPI), and possessed strong antioxidant properties due to its high α-tocopherol content. Conclusion: The findings of this study support the potential of safflower oil as a promising non-traditional edible oil, suitable for expanded cultivation in Egypt. Its favorable nutritional profile and stability make it a valuable addition to the dietary landscape. Keywords: Carthamus tinctorius L., safflower oil, edible oil gap, lipid nutritional indices, frying stability, non-traditional edible oils.

Signatures of local adaptation and maladaptation to future climate in wild Zizania latifolia

Global climate change poses challenges to agricultural production and food security. Assessing the adaptive capacity of crop wild relatives to future climate is important for protecting key germplasm resources and breeding new crops. We performed population genomics, genotype-environment association analyses, and genomic offset assessment of Chinese wild rice, Zizania latifolia, a crop wild relative and potential new grain crop, based on 168 individuals from 42 populations. We found two genetic lineages in Z. latifolia, corresponding to the south and north of its range, that diverged during the Late Pleistocene. We also identified lineage-specific positively selected genes associated with flower development and flowering, seed shattering, pathogen defense response and cold tolerance. We further found that populations from southeastern China are the most maladapted to future climate and should be prioritized for conservation. Our findings provide important clues for leveraging existing genetic diversity to identify important germplasm resources and create climate-resilient crops.

What do Lygus like? Looking for potential trap crops to reduce faba bean damage

Grain legumes, such as faba bean (Vicia faba L.), are crucial for protein supply and soil fertility enhancement through nitrogen fixation. However, faba bean cultivation is challenged by Lygus plant bugs (Hemiptera: Miridae), which cause significant crop damage and seed quality loss. This study aimed to evaluate Lygus preferences between faba bean and alternative crops to develop effective management strategies. We conducted choice bioassay experiments under laboratory conditions and field plot experiments. Laboratory results indicated sex-based host preferences, with males favoring faba beans and females preferring canola. Field studies showed that faba beans adjacent to canola had higher Lygus abundance and damage compared to those next to peas, flax, and safflower. Safflower and sunflower demonstrated potential as trap crops to reduce Lygus damage to faba beans. Our findings provide insights into Lygus behavior and suggest that a combination of trap cropping, and targeted insecticide use could mitigate the impact of Lygus infestations on faba bean cultivation.

Assimilation of Sentinel‐Based Leaf Area Index for Modeling Surface‐Ground Water Interactions in Irrigation Districts

AbstractVegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.

Evaluation of new sorghum varieties for grain production cultivated in mineral soil

Sorghum bicolor L. (Sorghum) has been recognized as the fifth largest cereal crop in the world, following maize, wheat, rice, and barley, with a mean annual production of more than 5.89 million tons over the past five years. Grain sorghum is one of the cultivated varieties of the sorghum genus. It is primarily used as human food and animal feed due to the beneficial effects of bioactive compounds in the grain. Sorghum is less popular and unrecognizable in Malaysia, as it was not included in the reports by the Agriculture Department regarding plantation areas. In Malaysia, sorghum is limited because this crop is considered new, and no studies were conducted on the adaptability of its varieties to Malaysian soil and local climatic conditions. Crop production practices such as variety selection, planting density and fertilization will affect yield production. Therefore, a study on grain sorghum varieties (Brachy Sorgho, Jumbo Super, India and Pacific Seed 99) was conducted to evaluate the growth and yield performance of sorghum. Jumbo Super had a significantly higher value for plant height, followed by Brachy Sorgho, India and Pacific Seed 99. India showed the highest value of leaf fresh and dry weight, but no significant difference compared with Brachy Sorgho. Brachy Sorgho recorded the highest value of stem fresh and dry weight, while the least was recorded for Pacific Seed 99. Brachy Sorgho and India showed significantly higher grain yields than Jumbo Super and Pacific Seed 99 in terms of panicle length, panicle weight without grain, grain weight without panicle per plant, number of grains per panicle, and 100-grain weight. The highest gross weight of grain sorghum is Brachy Sorgho (140.00 g/plant), which showed no significant difference compared to the India variety (131.13 g/plant). Meanwhile, Jumbo Super and Pacific Seed 99 varieties showed the lowest grain weight, 77.27 g/plant and 80.16 g/plant, respectively. Overall, all four varieties are adaptable in mineral soil and are considered potential alternative crops for grain production. Bachy Sorgho and India varieties showed the best performance on grain yield compared to Jumbo Super and Pacific Seed 99 varieties. Thus, Brachy Sorgho and India varieties can be recommended for future grain production in mineral soil.

Optimizing Soil Fertility through Machine Learning: Enhancing Agricultural Productivity and Sustainability

Nowadays, the sustainability of agriculture and food security have an increasing importance on soil fertility. Soil fertility is defined as the capacity of a land to grow crops and its potential crop productivity. However, factors such as increasing population, climate change, land use changes and environmental pollution threaten soil fertility. These threats can result in problems such as erosion, soil salinisation and organic matter depletion. Soil fertility is critical for the long-term health of agriculture and food security. Artificial intelligence techniques used to determine and manage soil fertility analyse the minerals present in the soil as well as other factors. These analyses assess the amount of minerals present in the soil, the availability of nutrients and important parameters such as pH. This information guides farmers in selecting the most appropriate crops. Furthermore, the integration of Internet of Things (IoT) technologies allows real-time monitoring of minerals and nutrients in the soil and optimising irrigation and fertilisation processes based on this data. These developments have the potential to improve soil fertility management and increase agricultural productivity.

The genome of Lupinus mutabilis: Evolution and genetics of an emerging bio-based crop.

Lupinus mutabilis is an under-domesticated legume species from the Andean region of South America. It belongs to the New World lupins clade, which groups several lupin species displaying large genetic variation and adaptability to highly different environments. L. mutabilis is attracting interest as a potential multipurpose crop to diversify the European supply of plant proteins, increase agricultural biodiversity, and fulfill bio-based applications. This study reports the first high-quality L. mutabilis genome assembly, which is also the first sequenced assembly of a New World lupin species. Through comparative genomics and phylogenetics, the evolution of L. mutabilis within legumes and lupins is described, highlighting both genomic similarities and patterns specific to L. mutabilis, potentially linked to environmental adaptations. Furthermore, the assembly was used to study the genetics underlying important traits for the establishment of L.mutabilis as a novel crop, including protein and quinolizidine alkaloids contents in seeds, genomic patterns of classic resistance genes, and genomic properties of L. mutabilis mycorrhiza-related genes. These analyses pointed out copy number variation, differential genomic gene contexts, and gene family expansion through tandem duplications as likely important drivers of the genomic diversity observed for these traits between L. mutabilis and other lupins and legumes. Overall, the L. mutabilis genome assembly will be a valuable resource to conduct genetic research and enable genomic-based breeding approaches to turn L.mutabilis into a multipurpose legume crop.

Evaluating trait variation and covariation in perennial, herbaceous crop candidate species using herbarium specimens

AbstractContemporary agriculture in many parts of the world consists of annual plant species grown in monoculture, but recently interest has focused on developing perennial, herbaceous species for use in perennial polycultures that offer greater ecosystem services relative to annual monocultures. Because few perennial, herbaceous species were domesticated by early farmers, one of the initial steps in identifying wild, perennial, herbaceous species for potential crop development is understanding patterns of variation and covariation among reproductive traits that underpin yield and vegetative traits. Herbaria can serve as a valuable resource for taxonomically broad surveys of phenotypic variation and covariation for pre-breeding. In this study, we used herbarium specimens to investigate trait variation and covariation in five genera of the legume family (Fabaceae). We addressed two questions: (1) do vegetative and reproductive traits vary as a function of life span (annual vs. perennial species), and are these differences consistent across genera?; and (2) how do vegetative and reproductive traits correlate with one another across lifespan and/or within genera? Reproductive traits and vegetative traits were measured on 535 herbarium specimens representing annual and perennial species of Astragalus, Lupinus, Phaseolus, Strophostyles, and Vigna. While species exhibited intra- and interspecific variation, few morphological differences were observed among annual and perennial species. Correlations among vegetative traits and among reproductive traits were mostly positive when significant, and perennial species generally exhibited more positive correlations between vegetative and reproductive traits than annual species. These data have potential relevance for perennial crop development, and provide further support that herbaria represent a rich, relatively untapped resource of information about plant trait variation and covariation.