Agronomic Factors Research Articles

Influence of Fertilizer on the Colour and Starch Properties of Yam (Dioscorea spp) Tuber

Yam is a tuber crop grown for food security, cultural value and income generation. Due to its high nutrient-demanding nature, soil fertility is required for its cultivation. fertilizer is a crucial agronomic factor in yam production, Still, yam farmers are skeptical about fertilizer usage because of their fear of it exerting poor qualities on yam tubers. Limited information on the effect of fertilizer on yam tuber quality is available. Four varieties each of Dioscorea rotundata (TDr9518544, Danacha, Hembakwase and Ojuiyawo) and Dioscorea alata (TDa291, TDa0200012, TDa0000194 and TDa9801176) were cultivated under non-fertilization, manure fertilizer (4.5t ha‑1 poultry manure) and NPK fertilizer (90, 50 and 75 Kg N, P, K ha-1). The field experiment was conducted in the International Institute of Tropical Agriculture, Ibadan, Nigeria, in the 2017 and 2018 cropping seasons. The harvested tubers were evaluated for colour indices (L, a*, b* and browning index) and starch properties (starch yield, amylose and amylopectin, starch granule size and shape). Data obtained was subjected to analysis of variance using Statistical Analysis System software (SAS) package and means were separated using Duncan multiple range test with a probability of p≤0.05. Fertilizer did not cause browning or any significant change in the colour indices of the tuber flesh. starch yield (18.83 to 28.3%), increase in amylose was observed in D. rotundata in the order; manureNPKcontrol with manure having 92.66% and NPK 40.2% increase. Fertilizer had no significant effect on the granule size and shapes of the starches. Hence, fertilizer, especially manure and NPK, should be used to cultivate yam on nutrient-depleted soil without detrimental tuber colour and starch.

The relative importance of herbicide use for conservation tillage adoption by U.S. corn and soybean producers.

Herbicide use is widespread in agricultural production to control weeds prior to and after planting and to "burndown" weeds in the spring for conservation tillage. Whether conservation tillage adoption leads to higher herbicide usage has been a question of policy relevance for decades in the United States. Older U.S. studies using standard statistical and economic techniques have not consistently demonstrated higher herbicide usage levels among producers practicing conservation tillage, but these studies did not fully account for other practices, economic, or agronomic factors. To provide a more timely and comprehensive understanding of the importance of herbicides to conservation tillage, this study achieves two objectives with the most recent, nationally representative data from the US Department of Agriculture. First, it describes trends and compares conservation tillage and herbicide usage among field corn and soybean producers, similar to previous studies using standard economic techniques. Second, a Classification and Regression Tree (CART) model is employed-a novel methodology relative to previous studies that offers distinct advantages over traditional regression modeling-to evaluate the importance of herbicide use for conservation tillage adoption while accounting for other factors. Pairwise mean comparisons for field corn and soybeans indicated that herbicide usage pre-emergence was significantly higher with conservation tillage, but there was no consistent, significant differences in herbicide usage post-emergence. The CART analysis (with prediction accuracy ranging from 68-72%) also showed that pre-emergent use of glyphosate was the strongest predictor (with predicted probabilities from 0.83-0.86) of conservation tillage for field corn in 2016 and soybeans in 2018. Other factors such as the use of crop rotations, highly erodible land, region, and farm size were also strong predictors of conservation tillage. These findings highlight the importance and complexity of herbicide use in the adoption of conservation tillage for U.S. field corn and soybeans.

Adaptive Agronomic Strategies for Enhancing Cereal Yield Resilience Under Changing Climate in Poland

Climate-driven changes have raised concerns about their long-term impacts on the yield resilience of cereal crops. This issue is critical in Poland as it affects major cereal crops like winter triticale, spring wheat, winter wheat, spring barley, and winter barley. This study investigates how soil nutrient profiles, fertilization practices, and crop management conditions influence the yield resilience of key cereal crops over a thirteen-year period (2009–2022) in the context of changing climate expressed as varying Climatic Water Balance. Data from 47 locations provided by the Research Centre for Cultivar Testing were analyzed to assess the combined effects of agronomic practices and climate-related water availability on crop performance. Yield outcomes under moderate and enhanced management practices were contrasted using Classification and Regression Trees to evaluate the relationships between yield variations and agronomic factors, including soil pH, nitrogen, phosphorus, potassium fertilization, and levels of phosphorus, potassium, and magnesium in the soil. The study found a downward trend in Climatic Water Balance, highlighting the increasing influence of climate change on regional water resources. Crop yields responded positively to increased agricultural inputs, especially nitrogen. Optimal soil pH and medium phosphorus levels were identified as crucial for maximizing yield. The findings underscore the importance of tailored nutrient management and adaptive strategies to mitigate the adverse effects of climate variability on cereal production. The results provide insights for field crop research and practical approaches to sustain cereal production in changing climatic conditions.

Straw Mulching and Weather Conditions Affecting the Trade-Off Between Grain Yield and Agronomic Traits of Maize

Straw mulching (SM) plays an important role in promoting the grain yield (GY) of maize under no-tillage conditions. However, there is still a lack of deep understanding on the interactive impact of SM and weather conditions on agronomic traits and the contributions to GY. This study selected a cornfield in the North China Plain as the research object and set up a straw management experiment, including SM and no straw mulching (NSM). The GY and agronomic traits of maize from 2018 to 2020 were monitored, and the relationship of agronomic traits with GY and the weather conditions were analyzed. The results show that SM promoted maize GY by 20.44%. Straw mulching increased the plant height, ear diameter, and ear height by 8.43%, 1.99%, and 12.65%, respectively. A correlation analysis showed that the ear length and ear height were the main factors affecting maize yield. Ear length was significantly correlated with kernel numbers per ear in SM. Growing degree days, hot dry wind, and air temperature significantly affected kernel numbers per ear and plant growth. This study highlights the contributions of agronomic factors to maize GY under SM and variable weather conditions and is helpful to improve cropland management.

Insights into management and physiological determinants of lowest pod height in soybean

AbstractHarvest losses caused by the low height of the lowest pods (LPH) are a significant issue in soybean cultivation. Minimizing these losses requires identifying management, physiological, and agronomic factors that interactively modulate LPH. Four studies were conducted to examine the relationships among soybean LPH, node and internode features, and light quality under different management practices. These practices included population density (19, 31, and 43 plants m−2) and row width (equidistant, 25, 51, and 76 cm), relative maturity (maturity group [MG] 0.8, MG 2.1, and MG 2.8), mulch color (gray bare soil, red mulch, and white mulch), and timing of stand reduction (V1, R3, R4, and R5 growth stages). An increase in population density from 19 to 43 plants m−2 led to an average increase in LPH of 28%, from 11.9 to 15.3 cm. LPH was not influenced by row width. Later maturing cultivars demonstrated the highest potential for enhancing LPH, with late AG2802 having a higher LPH (18.8 cm) than early AG0803 (12.4 cm). Data indicated that the elongation of internodes 10, 11, and 12, along with changes in the red to far‐red light ratio beneath the canopy, plays a pivotal role in determining the location of the lowest pods. Moreover, LPH is established around the R3 growth stage. Nevertheless, further investigations are warranted to gain a better understanding of how these parameters, individually and collectively, influence LPH in soybean.

Management of Nutrients in Soybean (Glycine max) Crops: A Review

Effective nutrient management is crucial for maximizing soybean (Glycine max) yield, promoting sustainable farming methods, and satisfying worldwide food requirements. This paper analyses significant discoveries and optimal methods in nutrient management for soybean crops, with a focus on the cruciality of efficient techniques to optimize yield, quality, and environmental conservation. Soybeans have distinct nutritional needs during their many growth stages, with nitrogen (N), phosphorus (P), potassium (K), and micronutrients playing vital roles in plant growth, yield formation, and stress resistance. Optimal fertilization, determined through soil analysis and considering the specific requirements of the crops, is crucial for providing sufficient nutrients while minimizing negative environmental consequences such as nutrient runoff, leaching, and greenhouse gas emissions. Novel developments and advancements, such as precision agriculture, variable rate fertilization, and smart fertilizers, present inventive methods to enhance the efficiency of nutrient management procedures. These technological improvements allow for the precise distribution of nutrients to specific areas, using data to make informed decisions and improve the efficiency of resources. This ultimately leads to better crop performance and sustainability. In addition, efficient nutrient management enhances soil health, increases resistance to both living organisms and non-living environmental pressures and ensures economic sustainability for farmers. Soybean producers may achieve optimal yields, quality, and profitability while minimizing environmental hazards by implementing integrated nutrient management systems, including organic amendments, and closely monitoring crop nutrition. To summarise, this review emphasizes the significance of nutrient management in soybean production, emphasizing the necessity for comprehensive strategies that take into account agronomic, environmental, and economic factors. Ongoing research, innovation, and information sharing are crucial for improving nutrient management strategies and guaranteeing the long-term sustainability of soybean cropping systems.

Prediction of deoxynivalenol contamination in spring oats in Sweden using explainable artificial intelligence

Weather conditions and agronomical factors are known to affect Fusarium spp. growth and ultimately deoxynivalenol (DON) contamination in oat. This study aimed to develop predictive models for the contamination of spring oat at harvest with DON on a regional basis in Sweden using machine-learning algorithms. Three models were developed as regional risk-assessment tools for farmers, crop collectors, and food safety inspectors, respectively. Data included: weather data from different oat growing periods, agronomical data, site-specific data, and DON contamination data from the previous year. Results showed that: (1) RF models were able to predict DON contamination at harvest with a total classification accuracy of minimal 0.72; (2) good predictions could already be made in June; (3) rainfall, relative humidity, and wind speed in different oat growing stages, followed by crop variety and elevation were the most important features for predicting DON contamination in spring oats at harvest.

Enabling Precision Agriculture through a Web-Based Fertilization Management System for Nawungan Selopamioro Fruit Orchards

Precision Agriculture (PA) is an integrated farming system based on information and technology for managing agriculture to identify, analyze, and manage spatial and temporal diversity information in specific locations to obtain optimum and sustainable benefits while minimizing unwanted environmental impacts. Fertilization is one of the crucial phases in agricultural production process considering technical cultivation aspects, costs, and environmental impacts. The current fertilization process at Kebun Buah Nawungan Selopamioro (KBNS) is still conventional, so there is no standard rule in determining the fertilization dose. Therefore, a PA approach is needed to provide suitable fertilizer doses for agricultural production needs. This objective of this study was to develop of a web-based fertilizer management system, integrating with orchard management to enhance accessibility and decision-making. The system calculates fertilizer requirements by analyzing soil nutrient availability (N, P, K), cultivation area, crop type and age, and available fertilizer types. The development followed the waterfall methodology, encompassing stages from requirement analysis to system maintenance. The outcome is a web application that manages land assets, administrative activities, and fertilizer needs tailored to specific land blocks, crop characteristics, and nutrient inventories. Subsequent validation against field conditions ensures the accuracy of its recommendations. Although comprehensive testing confirmed a 100% success rate in functionality, the system currently operates within a limited scope of variables. Future enhancements are planned to incorporate broader agronomic factors, such as soil pH and texture, to augment the system's precision. Despite its limitations, this system represents a significant technological advance in precision agriculture, promising to improve fertilizer application efficiency and support sustainable farming practices.

Microgreens Production: Exploiting Environmental and Cultural Factors for Enhanced Agronomical Benefits.

An exponential growth in global population is expected to reach nine billion by 2050, demanding a 70% increase in agriculture productivity, thus illustrating the impact of global crop production on the environment and the importance of achieving greater agricultural yields. Globally, the variety of high-quality microgreens is increasing through indoor farming at both small and large scales. The major concept of Controlled Environment Agriculture (CEA) seeks to provide an alternative to traditional agricultural cultivation. Microgreens have become popular in the twenty-first century as a food in the salad category that can fulfil some nutrient requirements. Microgreens are young seedlings that offer a wide spectrum of colours, flavours, and textures, and are characterised as a "functional food" due to their nutraceutical properties. Extensive research has shown that the nutrient profile of microgreens can be desirably tailored by preharvest cultivation and postharvest practices. This study provides new insight into two major categories, (i) environmental and (ii) cultural, responsible for microgreens' growth and aims to explore the various agronomical factors involved in microgreens production. In addition, the review summarises recent studies that show these factors have a significant influence on microgreens development and nutritional composition.

Fertilization and tillage influence on soil organic carbon fractions: A global meta-analysis

Soilorganic carbon(SOC), as the largest terrestrial carbon pool, plays an important role in global carbon cycling, which is significantly impacted by agricultural practices. However, our ability to accurately detect and predict the impacts of fertilization and tillage onSOC dynamicsis still limited. Investigating the effects of fertilization and tillage on different SOC fractions [i.e. mineral-associated organic carbon (MAOC), particulate organic carbon (POC), free POC (frPOC), occluded POC (oPOC), coarse POC (cPOC), and fine POC (fiPOC)]can aid in the understanding of overall SOC accumulation and stabilization. To this end, we evaluated the fertilization and tillage influences on SOC fractions through a global meta-analysis. We also quantified the role of environmental and agronomic factors in modulating these effects. Fertilization increased SOC fractions by mean percent change (MPC)13 %-77 %, while tillage decreased by MPC 4 %-63 %. Among them, cPOC was the most sensitive to fertilization, while frPOC had the highest sensitivity to tillage. MAOC was the least sensitive to both practices. The application of organic fertilizer increased MAOC, SOC, and POC the most (MPC 20 %-77 %), while mineral-organic fertilizer increased frPOC, oPOC, fiPOC, and cPOC the most (MPC 81 %-126 %). Fertilization in alkaline soils with warm and humid (MAT=16–24 °C, MAP>1000 mm) climate could maximally increase SOC contents from various fractions in surface layer (<20 cm depth), particularly when the altitude was 500–1000 m. However, tillage in acidic soils with low temperatures and rainy climate (MAT=8–16 °C, MAP>1000 mm) reduced the contents of SOC fractions the most in deep layer (>40 cm depth), especially at altitudes greater than 2000 m. Whether under fertilization or tillage, POC (occupying 62 %-74 %) consistently contributed more to SOC than MAOC (26 %-38 %). Overall, we suggest that SOC fractions should be prioritized over total SOC when evaluating the effects of site-specific management strategies on carbon sequestration in agricultural lands.

Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations

AbstractAgriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditions, soil types, and limited access to modern farming practices. Developing intelligent agricultural systems becomes imperative to address these challenges and enhance agricultural productivity. Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. The proposed system utilizes historical data on climatic conditions, soil properties, crop yields, and farmer preferences to provide personalized crop recommendations. The goal of this study is to appraise the efficacy of nine distinct ML models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Bagging (BG), AdaBoost (AB), Gradient Boosting (GB), and Extra Trees (ET) to generate practical recommendations for crop selection. Numerous preprocessing methods are employed to cleanse and normalize the data, thereby ensuring its appropriateness for model training. The ML models are trained using historical data sets, including temperature, rainfall, humidity, soil pH, and nutrient levels, where crop yields are correlated with environmental and agronomic factors. The models undergo fine-tuning through methods such as cross-validation to enhance their performance and ensure robustness. Among those models, Radom Forest has achieved the highest accuracy (99.31%). The proposed Machine Learning-based crop recommendation system offers a promising approach to addressing the challenges faced by the farmers. By leveraging advanced data analytics and artificial intelligence techniques, the system empowers farmers with timely and personalized recommendations, ultimately leading to improved agricultural productivity, food security, and economic prosperity.

Abiotic stress responses in crop plants: A multi-scale approach

Population stress has compelled the whole world to think about increasing, protecting, and finding ways to develop the best plant species for assured productivity in spite of all environmental odds. In addition to climate change, fluctuations in nature and human activities pose serious environmental degradation, threatening global food security through physical stress in the environment. Significant environmental constraints on agricultural yield worldwide include salt stress, water deficiency stress, nutritional imbalances (including mineral toxicity and deficiencies), and temperature extremes. Abiotic factors, such as agronomic factors, climatic factors, and nutrient availability in the soil, influence crop yield. Plants initiate and develop various possible stress mechanisms for their survival, which can be molecular, cellular and physiological. Abiotic stress has a high impact on crop growth and productivity, either in single forms or in combined forms. For example, drought stress causes a decrease in leaf area, plant height, and crop development. Cold stress reduces plant development and crop efficiency, resulting in productivity loss. Salinity stress not only contributes to water stress in plants, but it can also adversely influence cytosolic metabolism, cell development, membrane function, and increase reactive oxygen species (ROS) generation. Higher concentrations of CO2 could potentially improve global precipitation, resulting in increased rainfall, which can adversely affect crop development. Crops under excessive water stress have a lower percentage of amylose but a higher crude protein content. This in turn, affects the quality and quantity of crop production by hindering seed germination and causing growth damage due to the combined effects of higher osmotic potential and ion toxicity. In response to abiotic stress, plants evolve a variety of escape-avoidance and tolerance mechanisms, which include physiological adaptation and integrated cellular or molecular responses. Therefore, the main purpose of the current review paper is to investigate the effect of abiotic stress on morpho-physiological, biochemical and molecular activities in various crops. Moreover, we concentrate on crop inter-relativity with abiotic stress to react to and adapt to for survival, which can be a basic roadmap for the selection of species or the development of new tolerant species in the future.

Developing a new active canopy sensor- and machine learning-based in-season rice nitrogen status diagnosis and recommendation strategy

ContextTraditional critical nitrogen (N) dilution curve (CNDC) construction for N nutrition index (NNI) determination has limitations for in-season crop N diagnosis and recommendation under diverse on-farm conditions. ObjectivesThis study was conducted to (i) develop a new rice (Oryza sativa L.) critical N concentration (Nc) determination approach using vegetation index-based CNDCs; and (ii) develop an N recommendation strategy with this new Nc determination approach and evaluate its reliability and practicality. MethodsFive years of plot and on-farm experiments involving three japonica rice varieties were conducted at fourteen sites in Qixing Farm, Northeast China. Two machine learning (ML) methods, random forest (RF) and extended gradient boosting (XGBoost) regression, were used to fuse multi-source data including genotype, environment, management, growth stage, normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) from portable active canopy sensor RapidSCAN. The CNDC was established using NDVI and NDRE instead of aboveground biomass (AGB) measured by destructive sampling. A new in-season N diagnosis and recommendation strategy was further developed using direct and indirect NNI prediction using multi-source data fusion and ML models. ResultsThe new CNDC based on NDVI or NDRE explained 94−96 % of Nc variability in the evaluation dataset when it was coupled with environmental and agronomic factors using ML models. The ML-based PNC and NNI prediction models explained 85 % and 21–36 % more variability over simple regression models using NDVI or NDRE in the evaluation dataset, respectively. The new in-season N diagnosis strategy using the NDVI and NDRE-based CNDCs and plant N concentration (PNC) predicted with RF model and multi-source data fusion performed slightly better than direct NNI prediction, explaining 7 % more of NNI variability and achieving 89 % of the areal agreement for N diagnosis across all evaluation experiments. Integrating this new N management strategy into the precision rice management system (as ML_PRM) increased yield, N use efficiency (NUE) and economic benefits over farmer’s practice (FP) by 7–15 %, 11–71 % and 4–16 % (161–596 $ ha−1), respectively, and increased NUE by 11–26 % and economic benefits by 8–97 $ ha−1 than regional optimum rice management (RORM) under rice N surplus status under on-farm conditions. ConclusionsIn-season rice N status diagnosis can be improved using NDVI- and NDRE-based CNDC and PNC predicted by ML modeling with multi-source data fusion. ImplicationsThe active canopy sensor- and ML-based in-season N diagnosis and management strategy is more practical for applications under diverse on-farm conditions and has the potential to improve rice yield and ecological and economic benefits.

A systematic literature review on environmental, agronomic, and socioeconomic factors for the integration of small-scale coffee producers into specialized markets in Oaxaca, Mexico

The coffee sector’s challenges, such as low and volatile prices, prompt adopting strategies focused on product differentiation, including high-quality specialty coffee. Nevertheless, documented evidence shows that small producers in Oaxaca, Mexico, are not equally benefiting from this market opportunity compared to other states in Mexico, such as Veracruz or Chiapas. This review article identifies and analyzes the factors in integrating small Oaxacan producers into the specialty market. A total of 138 scientific contributions were analyzed, emphasizing three thematic axes: (1) Trends and behavior of the coffee market. (2) Factors to enable the production of high-quality coffee, and (3) Current situation and challenges of small coffee producers in the specialized market. We found that coffee quality depends on the environment (mainly altitude, temperature, and geographical conditions) and agronomic, genetic, and socio-economic factors. Moreover, agronomic and genetic factors should focus on choosing coffee varieties suited to the environmental conditions of the farm and adopting innovations in production, harvest, and post-harvest processes. The most significant socio-economic factors that hinder small coffee producers’ participation in the specialized market include but are not limited to, lack of information, social connection, and financial resources. However, in the literature analyzed here, it was also possible to identify specific farmworker experiences that operate with a community approach and have inserted their small producers into high-quality market transactions. For this reason, this article suggests that the standardization model of quality from the collective approach within the community-based enterprise is a sustainable and inclusive alternative to achieve the goal: the insertion of small Oaxacan coffee producers into the more significant specialty market.

Effect of bio-fertilizers and seaweed extract on growth and yield of wheat (Triticum aestivum L.) under different irrigation regimes: Two-year field study

Wheat productivity is constrained by genetic, agronomic, and climate factors, though it is an important crop for food production worldwide. The present study evaluated the effect of bio-fertilizer consortia and seaweed extracts on the growth and yield of two wheat varieties under different irrigation regimes in a field study. This experiment was conducted in a split-split plot based on a randomized complete block design with four replications in 2018 and 2019. Irrigation treatments were the main factor, wheat variety (Sardari and Sirvan) the sub-factor, and bio-fertilizers the sub-sub-factors. The results showed that irrigation regimes significantly improved leaf width, number of leaves, fresh weight of roots and shoots, osmotic potential, leaf water content, and number of stomata respectively by 57.53, 38.59, 106.65, 135.29, 87.92, 14.22 and 13.77, 88.02 and 96.11 percent compared to dry-land conditions. Applying one- and two-times irrigation increased grain yield by 51% and 79%, respectively, and the response varied in wheat varieties. Sardari variety due to having smaller leaf dimensions (Leaf length and width) and lower fresh and dry weight of roots and shoots, as well as lower leaf and tissue water content, had lower grain yield than the Sirvan variety. All the bio-fertilizers positively impacted the growth and yield of both varieties. However, the highest average grain yield in the first and second years of the experiment (with an average of 5226.25 and 4923.33 kg/ha, respectively) were found under the combined application of Mycorrhiza + Nitrozist and Phosphozist + Seaweed extract. The results of the present study underscore the importance of irrigation regimes and consortia of bio-fertilizers for improving grain yield. This study also highlighted the resilience of the studied wheat varieties and bio-fertilizers to projected climate changes. These findings could provide insights into adaptive strategies for mitigating the impact of climate change on wheat production.

Typology of smallholder maize farmers in South-Kivu, Eastern D.R. Congo: implications in improving farming practices and markets

Understanding the typology of smallholder maize farmers is crucial to elucidate the diversity in local farming systems, and therefore, devise effective interventions and policies that maximize the impact of agricultural innovations in South-Kivu province, eastern Democratic Republic of Congo (DRC). This study aimed at characterizing maize farmers in South-Kivu province and clustering them based on various socioeconomic and agronomic factors to identify specific production constraints and devise more effective interventions. A household survey among 300 South-Kivu maize farmers allowed to identify four distinct clusters based on farm size, land use, income, and agricultural technology adoption. The clusters ranged from resource-poor smallholder (Types 1 and 2) to medium-resource semi-innovative producers (Type 3) and highly equipped innovative farmers (Type 4). Key discriminative factors included total farm size, maize farm size, farming experience, access to agricultural credit, income, and innovation adoption rate. This study revealed specific constraints for each cluster, including low soil fertility, poor weed management, low market access, and labor scarcity. This study’s findings demonstrate the value of farm typology in guiding targeted interventions to address specific constraints and improve maize productivity, and suggest specific strategic directions for research and development projects in rural areas of the South-Kivu province.

Tipping points and farmer decision-making in European permanent grassland (PG) agricultural systems

Permanent grasslands (PG) provide multifunctional ecosystem services (ES) in Europe and globally, which are threatened by both increased farming intensity and land use change in marginal areas. Farm management decisions can represent critical thresholds, or behavioural “tipping points”, in the agricultural system. Decisions are influenced by a combination of agronomic, policy and social factors. Transformation of PG systems can be facilitated through positive tipping points and relevant policy implementation to ensure sustainable PG systems. The aim of this research was to understand the drivers of decisions regarding land use changes and management towards critical positive and negative tipping points across five biogeographic zones in Europe. Interview methodology assessed farmers’ preferences and priorities regarding the adoption of sustainable PG systems. Participants were selected from five case study countries, each representing a different biogeographic zone in Europe (Continental/Pannonian: Czech Republic, Boreal: Sweden, Mediterranean: Spain, Alpine: Switzerland, and Atlantic: UK). The sample also covered three farming intensity types within these biogeographic zones: high input/intensive conventional farms (≥1.0 LU/ha); low input/extensive conventional farms (<1.0 LU/ha); and certified organic farms. In total, 373 farm interviews were obtained from the case study countries between October 2020 and October 2021. The analysis focuses on drivers of change and considers tipping points across these countries, considering case studies of land use changes (specifically land abandonment) and land management practices (specifically changes in stocking rates). The most common reasons for PG management changes towards either intensification or extensification were economic. Farmers require policy support to increase provision of non-market ES, while rebalancing subsidies can deliver environmental ES at scale through abandonment (e.g., through the creation of specific habitats that support some threatened species). Agri-environment schemes (AES)and subsidies could be more flexible to allow farmers to better adapt grassland management to local production conditions and unpredictable circumstances such as droughts, floods, or market shocks. To maintain PG that delivers more goods and services, financial compensation for ES delivery was perceived to be the most significant support mechanism needed, while easier access to ES provision expertise through extension or consultancy services is considered important factor.

Physiochemical and Microbial Quality of Melon and Bush Mango Seeds and Milled Products in Local Markets ln Rivers State,Nigeria.

Risk of food borne infection contamination of food, in Africa is increased due to environmental, agronomic and socio-economic factors. This study assesses the microbial quality of melon and bush mango displayed in the local markets, aiming to provide valuable insights into the safety and quality of these widely consumed food products. Standard procedure for microbiological and proximate analysis were employed. The heterotrophic bacteria count ranged from 8.5 x 10⁵ cfu/g to 1.35 x10⁶ cfu/g. The total staphylococcus count ranged from 1.2 x 10³ cfu/g to 4.35 x 10³ cfu/g. The coliform count ranged from 2.8 x 10⁴ to 7 x 10⁵, Total Samonella count ranged from 4 x 10² cfu/g to 1.5 x 10³ cfu/g and Fungi count 6.5 x 10² cfu/g to 2.5 x 10³ cfu/g. Morphological and Biochemical characterization of heterotrophic bacteria isolated from both melon and bush mango identified nine genera, including Staphylococcus (14.6%) Bacillus (20%), Corynebacterium (5.6%) Enterobacter (4.4%), Micrococcus ( 3.3%) Pseudomonas (12.2%), Escherichia coli (13.3%), Salmonella (11.1%) and Proteus (11.1%). Fungal species like Penicillium spp., Fusarium spp., Aspergillus flavus, Candida spp., Mucor spp., Saccharomyces sp., and Aspergillus niger were isolated from both seed and milled seeds samples of melon and bush mango. The frequency of occurrence revealed variations among bacterial isolates in melon and bush mango seeds. The most frequent fungal species included Candida spp. (9.4%) Aspergillus niger (20%) Mucor (9.4%), Fusarium (11.4%), Aspergillus flavus (9.4%), Penicillium (13.2%), Rhizopus spp (3.7%) and Saccharomyces (15%). Proximate and heavy metal analysis indicated variations in the composition of Carbohydrate (7.11%), Lipid (15.03%), Ash (1.89%), Moisture (7.31%), Protein (23.86%), Crude Fiber (12.66%), Lead (0.213 mg/kg), Copper (2.11mg/kg), calcium (0.49mg/kg) and Cadmium (0.0169mg/kg) in melon and bush mango, Carbohydrate (15.03%), Lipid (58.24%), Ash (1.74%), Moisture (10.48%), Protein (11.95%), Crude Fiber (2.64%), Lead (1.7903mg/kg), Copper (2.11mg/kg), calcium (0.148mg/kg) and Cadmium (0.000385mg/kg). The presence of heterotrophic bacteria in such ranges suggests potential differences in hygiene practices during cultivation, processing, and storage. Therefore; Implement stringent quality control measures during the production, processing, and storage of both melon and bush mango seed.

Enhancing lucerne (Medicago sativa) yield and nutritional quality: a meta-analysis of fertilization types and environmental factors in China.

Lucerne (Medicago sativa), is a cornerstone of China's livestock industry, however, due to the backward agronomic strategies and technology, lucerne in China faces cultivation challenges that result in lower productivity and quality than global standards. Therefore, we undertook a meta-analysis to evaluate the impact of five distinct fertilization types on lucerne yield and nutritional quality in various locations in China. The fertilizer practices included manure application, combined mineral fertilizer and manure application (FM), biological fertilizer application, unbalanced application of two or more mineral fertilizer types, and balanced mineral fertilizer application. Furthermore, we investigate influential factors of yield and quality of lucerne under fertilization, including climatic variables (mean annual precipitation, mean annual temperature), initial soil properties (soil organic carbon; total nitrogen, pH), and agronomic factors (seeding rate, harvest frequency, and lucerne stand age). Our study analyzed 53 published papers to discern the most beneficial fertilizer for enhancing lucerne yield and nutritional quality. The results showed that the fertilizer practices, on average, significantly improved yield by 31.72% and crude protein content by 11.29%, with FM emerging as the most effective, this is because mineral fertilizers provide available nutrients for lucerne, manure provides essential organic matter for microorganisms and improve soil properties. In addition, the fertilizer practices significantly reduced neutral and acid detergent fiber contents by 6.28% and 8.50%, respectively, while increasing ash content and relative feeding value. Furthermore, climatic variables, soil properties, and planting system factors such as sowing date and harvest frequency significantly affected yield and nutritional quality. The practical implications of our results emphasize the need for balanced and strategic fertilizer application to optimize lucerne production and highlight the potential to adjust cultivation practices according to environmental conditions. Balanced and strategic fertilizer application can simultaneously improve soil properties, enhance soil carbon sequestration, and reduce the emission of greenhouse gases from the soil, which is a vital measure for realizing sustainable agricultural development.

Maximizing Antioxidant Potential in Picual Virgin Olive Oil: Tailoring Agronomic and Technological Factors with Response Surface Methodology.

Over the past years, a prolonged drought has affected Spain, raising significant concerns across various sectors, especially agriculture. This extended period of dry weather is profoundly affecting the growth and development of olive trees, potentially impacting the quality and quantity of olive oil produced. This study aims to assess the impact of agronomic factors, i.e., olive maturation and irrigation management, as well as the technological factors involved in the production process, on the antioxidant content of Picual virgin olive oil. Mathematical models were developed to maximize the concentration of polyphenols, orthodiphenols, chlorophylls, carotenes, and tocopherols in olive oils. Findings indicate that increasing the malaxation temperature from 20 to 60 °C and reducing the mixing time from 60 to 20 min positively influenced the polyphenol and orthodiphenol content. Although irrigation did not significantly affect the polyphenols, pigments, and α-tocopherol contents, it may enhance the β- and γ-tocopherol content. Optimal conditions for producing antioxidant-enriched virgin olive oils involved olives from rainfed crops, with a moisture index of 3-4, and a 60-min malaxation process at 60 °C. Under these conditions, the total phenol content doubled, pigment content increased fourfold, and α-tocopherol content rose by 15%. These findings provide relevant knowledge to interpret the year-to-year variation in both organoleptic and analytical profiles of virgin olive oils.