Journal of Agriculture and Food Research最新文献

Double-cropping is an interesting diversification strategy to increase profitability and sustainability. Besides, by including legumes, it may reduce the N inputs dependency. However, its management with flood irrigation can be challenging. The was the evaluation of the impact of two double-cropping and different nitrogen (N) rates on productivity and N dynamics under flood irrigation. Two double-cropping (barley-maize; pea-maize) and three N rates (unfertilized, 0N; medium: MN; high rate, HN) were evaluated in a field experiment located in NE Spain during two years. In barley-maize, N rates were 0, 125 and 250 kg N ha⁻¹, and 0, 200 and 400 kg N ha⁻¹ for barley and maize phases, respectively. In pea-maize, pea did not receive N, and the MN and HN rates in the maize were reduced (150 and 350 kg N ha⁻¹, respectively).

Crop productivity and N uptake were evaluated. The economic return and N use efficiency (NUE) were calculated. Soil inorganic N (Nmin) was measured after harvest.

The pea-maize double-cropping allowed the saving of N fertilizer without yield penalties. Besides, a pre-crop effect of pea in the maize yield was observed. Regarding the cropping system, pea-maize obtained a higher economic return the first year. Besides, the economic return was higher the first year, due to crop penalties. In general, NUE was greater in 0N, and no differences were observed between fertilized treatments. Soil Nmin after pea/barley was higher the first year, coincident with a higher productivity. After maize harvest, the barley-maize led to larger residual Nmin.

Therefore, in this study, pea-maize allowed a reduction in the N rate, showed greater potential to increase the economic return and reduced the N leaching risk. However, crop yield penalties associated with flood-irrigation, indicate that farmers should be aware of facing a more challenging management or consider other irrigation method to ensure profitability.

An ongoing and global agenda is the food security policy debate in rural Ethiopia of households to enhance resilience at the individual and household level for tackling food insecurity. Therefore, this study investigated determinants of household resilience to food insecurity in rural dwellers of Mekiet District. A cross-sectional research design was conducted in two agro ecological zones of the Mekiet district, lowland, and midland, involving 228 respondents. The study employed a quantitative and qualitative approach, with multi-stage sampling techniques results employed to select sample households by using two-stage factor analysis. The result from factor analysis using the RIMA approach showed that the latent variables of adaptive capacity, asset, and social safety net have a positive value of (0.76), (0.51), and (0.77), respectively, and in the contrast, access to essential services has a negative load of (-0.61), and all variables were found to be statistically significant. All those loads were significant, with an absolute value of factor loadings greater than 0.364, which is recommended for a sample size of 200 and above. Those variables were also highly correlated with resilience capacity. The study recommended that resilience programs should be implemented at district, community, and household levels to enhance resilience to food insecurity.

The main concerns regarding the management of soilborne pathogens of vegetable crops through fumigants are due to their detrimental effects on the human health and environment. Due to global restrictions, soil solarization alone or combined with low-impact measures are being re-evaluated as eco-friendly alternatives to replacement of phasing-out or banned fumigants. Three experiments were performed to compare the performance of traditional and innovative films in increasing thermal performance under different partially opened or kept closed greenhouses. The effects of treatment alone or combined with Brassica pellet (Biofence®) incorporation were tested in suppressing microsclerotia of Verticillium dahliae, causing wilt of tomato and vegetable crops. Although inducing different thermal regimes at depths 15 and 30-cm depths, all films were effective in reducing microsclerotia in soil compared to the bare plots under different greenhouse conditions. The performances achieved in closed greenhouse were better than those achieved in greenhouses with the side openings kept open. In these latter conditions, green-colored ethylene-vinyl-acetate (EVA) and uncolored EVA film well performed when combined with Brassica pellet, achieving better results than those obtained in the plots mulched with film or amended alone, respectively. Smoky gray low-density polyethylene (LDPE), followed by uncolored EVA and polyamide virtually impermeable film (PA-VIF) were the most effective in reducing/suppressing V. dahliae microsclerotia within 3-to-8 weeks of treatment in closed greenhouse whereas green EVA showed lesser performance. PA-VIF and EVA green film are attractive since they may be used in combination with biofumigants or left on as mulching for weed control of crops. This paper indicates that sustainable solarization-based approaches with different films alone or integrated with Brassica incorporation can also be successfully pursued against thermo-tolerant pathogens as V. dahliae. Moreover, our findings preliminarily show that the choice of film and treatment duration should be modulated based on the greenhouse operative conditions.

As has been confirmed in our previous study, red onion is a promising source of inulin-fructan, inulin neoseries fructan, and their fructooligosaccharides (FOSs). Short-chain FOSs (SCFOSs) are potential prebiotics as they provide many health promoting benefits. However, short-chain inulin neoseries oligosaccharides isolated from plants have scarcely been studied. After prebiotic production, a purification step is essential to produce a functional prebiotic. The aim of this study was to develop new processes of integrated enzymatic hydrolysis of crude red onion extract and yeast treatment for production and purification of SCFOSs. Endo-inulinase is a key enzyme for hydrolysis of crude red onion extract to produce SCFOSs, while Candida orthopsilosis FLA44.2 was employed for purification of the produced SCFOSs. Three different strategies, including two-step (TSP), simultaneous (SP), and semi-simultaneous (SSP) production processes, were designed and investigated as potentially simple, cost-effective, and timesaving production processes. The SP and SSP processes met the objective criteria by exhibiting desirable yields of SCFOSs and the optimal purity of SCFOSs when compared with the TSP process. Notably, the SP process was highlighted as it was simpler than the SSP process. Under optimal conditions (0.4 U of endo-inulinase/g total fructans, 5 % v/v of yeast inoculum and reaction time of 72 h), the SP process conducted in 1-dm³ flasks produced SCFOSs of 87 g/dm³ with neo-GF₂ as the main constituent (42 g/dm³) and a purity of 100 %. The ratio of total SCFOSs to total fructans was 0.93 indicating that fructans of the red onion extract have been transformed to SCFOSs.

Despite established evidence about the environmental and health benefits of alternative protein foods (APF), considerable knowledge gaps and misconceptions remain toward APF. Drawing on a national survey (1800+ responses), the study explores Canadians' experiences of APF and intentions to alter current dietary patterns focusing on four APF – plant-based alternative proteins (PBAP) – plant-based (PB) milk, PB meat, lab-grown (LG) meat, and other alternative proteins (OAP) – and two animal-based foods (ANBF)– milk and meat. Data were collected based on a proportional stratified sampling method from all regions of Canada and analyzed using econometric models. While PB milk was the most consumed APF, followed by PB meat, LG meat was the least tried among participants. Perceived complexity of APF discouraged individuals from cutting back on ANBF, while increasing the consumption of APF. Perceived relative advantage was positively related to the adoption of APF. Perceived compatibility strongly influenced individuals’ adoption of PB meat and OAP. Likewise, perceived trialability significantly influenced the adoption of PB milk and PB meat. Despite the perception that APF may have high health related risks associated with processing, additives, calorie and sugar content, this did not prevent individuals from consuming PB milk and OAP. However, perceived risks did affect intentions to alter dietary patterns in the next 12 months. Additionally, meat attachment and sustainability orientation significantly predicted current consumption decisions of APF and intentions to alter dietary patterns. Although sustainability orientation motivates dietary changes, it did not always lead to extreme shifts. Finally, several demographic variables (age, gender, and education), dietary preferences (being omnivore), and residential area and region had influence on current consumption decisions and future intentions. In conclusion, by controlling several factors and through a comparative analysis of various protein sources, the study offers insights into the interplay of innovation-adoption characteristics, perceived risks, meat attachment and sustainability orientation in understanding dietary choices and provides some implications for industry stakeholders and policies promoting APF.

This paper delves into the challenges impeding the seamless integration of artificial intelligence (AI) within the food supply chain (FSC) and introduces a novel methodological framework that combines the NK Model with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. Through an exhaustive literature analysis and expert discussions, the research identifies and categorizes significant obstacles to AI deployment in the FSC. These hurdles include the imperative for a skilled labor force, financial limits, regulatory complexity and technological limitations. The unique DEMATEL-NK approach highlights the interconnected nature of these barriers, pinpointing the most critical impediments. The study's implications extend to the broader domains of AI adoption in agriculture and the food industry, offering a nuanced perspective for policymakers, industry stakeholders, and researchers. The findings underscore the imperative of overcoming these barriers for the successful implementation of AI technologies in the FSC, promising advancements in efficiency, quality, and sustainability. The innovative methodology not only sheds light on the interconnectedness of these barriers but also provides a systematic approach for prioritizing and implementing solutions. This research offers a fresh viewpoint on barrier relationships, guiding decision-makers in crafting effective strategies and interventions to propel AI integration in the FSC forward.

Coffee (Coffea arabica L.) is a globally important agricultural crop with increasing quality requirements. However, the challenges posed by climate change impact cup quality. Therefore, the objective was to evaluate the influence of tree diversity and crop segmentation under agroforestry systems on the sensory characteristics of coffee. The study employed a 4A*3B factorial design, with factor A: agroforestry systems (mixed system, shade-free system, system with Erythrina edulis and system with Inga edulis) and factor B: segmented harvest (upper, middle, and lower third cherries). Fragrance/aroma, flavor, residual flavor, acidity, body, uniformity, balance, clean cup, sweetness, cupper's score, and final score were evaluated. The results suggest that coffee trees planted in mixed systems provide the best sensory characteristics of coffee in the cup since the highest averages were obtained in most of the variables evaluated. Likewise, it is concluded that the diversity of trees (mixed system and system with Erythrina edulis) has a positive influence on the sensory quality of the coffee, reaching a score of 82.86 and 82.64 for the mixed system and the system with Erythrina edulis respectively; categorizing them as specialty coffees. Agroforestry systems constitute an option for developing sustainable agriculture while improving the sensory quality of coffee in the Peruvian Amazon.

The current era of rapid climate change necessitates greater emphasis on wild, often underutilized yet sturdy, edible plants that are capable of growing in harsh arid lands. When compared to more popular crops like rice, these are often of traditional significance and more region-specific; but needing less chemical fertilizers, pesticides and irrigation water, they can not only provide food and nutrition in a sustainable manner but also medicinally valuable compounds (nutraceuticals) to target various communicable and non-communicable diseases. These bioactive metabolites could also serve as markers for in-process quality control of herbal formulations and as metabolic biomarkers. Of late, a few of the common food crops across the world have benefited from the use of technological interventions, employing various Internet of Things (IoT) devices and sensors to collect data on the farm and conduct agro-food specific analytics. Machine Learning (ML) and deep learning (DL) have found application in numerous facets of agriculture, particularly in tasks such as yield prediction, disease detection, weed detection, crop recognition, and assessing crop quality at pre-harvest, harvest, and post-harvest stages. ML technology also has shown potential to be effectively employed at various stages of bioactives discovery, encompassing target identification, compound screening, lead discovery, as well as pre-clinical and clinical development phases. However, the usage of these modern technologies has been less explored in the desert plants of the world. The current article reviews a few available examples and highlights the potential of employing ML and DL technologies in edible plants of the world, with a focus on sustainable desert flora, for achievement of multidisciplinary objectives, that is, agro-food production, food safety and bioactives discovery.

Consumer ethnocentrism plays a key role in the markets of developed countries when governments take protectionist measures due to economic crises and downturns. Consumer ethnocentrism appears to be stronger in relation to the choice and purchase of food and beverages. The purpose of the current study is to investigate the relationship between consumer ethnocentrism and socio-demographic characteristics in the case of a Hungarian national ‘geographical indication’ (GI) spirit called pálinka. The literature emphasises the importance of testing the impact of consumer ethnocentrism on different products, and research on national and GI products is quite limited. Although pálinka is one of the best-known products in Hungary, the perception and quality of the alcoholic beverage have undergone significant changes in recent decades. The analysed sample, representative of the Hungarian alcohol-consuming population, contains the answers of 760 respondents. Consumers' Ethnocentric Tendencies Scale items were used to cluster consumer groups with different perceptions using latent profile analysis (LPA). According to the results, consumer ethnocentrism remains a significant issue in the Hungarian pálinka market despite growing globalisation and consumers' openness to foreign products. There is a higher level of ethnocentrism with national and GI products, which needs to be taken into account by market participants. With the help of cluster analysis, four consumer groups with different socio-demographic characteristics were identified. The results may help actors in the pálinka industry and their competitors (e.g., vodka and whiskey producers and distributors) to understand the Hungarian alcohol market and related consumer groups in respect of ethnocentrism.

This study aims to optimize the parameters of the foam-mat drying to produce banana powder. Input parameters of the foam drying such as foaming agent (egg albumin) concentration used from 5 to 15 %, the foam stabilizer (maltodextrin) used from 1 to 3 % and the drying temperature varied between 60 and 80 °C. Factors with 3 levels are arranged according to the Box-Behnken design, which further modelled by RSM (response surface methodology) and ANN (artificial neural network), and optimized. The drying rate and total polyphenol content (TPC) of banana powder under the studied conditions were determined as the target output. The moisture, color, total polyphenol, antioxidant activity and some physical parameters of final fine powder were analyzed. Increasing temperature has increased the drying rate. In addition, increasing the concentrations of egg albumin and maltodextrin maintained the highest TPC and maximum drying rate. ANN model showed the higher forecasting capacity than that of RSM. Moreover, simultaneous optimization of two responses (TPC and drying rate) was selected to maximize the desired value at the concentration of albumin, maltodextrin and drying temperature of 11.86 %, 1.92 %, 74.94 °C, respectively, corresponding to the highest TPC value and drying rate of 1.31 mgGAE/g DW and 2.48 g water/g dry matter/min. At this condition, the drying time was recorded as 103 min. Validation of the optimal ratios showed that the experimental values of TPC and drying rate were in good agreement with the model predicted data. The moisture content and water activity of product were found to be 5.87 ± 0.07 % and 0.37 ± 0.01. The product had bright colors with L*, a* and b* values were measured as 86.4 ± 0.5, 1.75 ± 0.08 and 15.2 ± 0.3, respectively. The high DPPH radical scavenging activity was detected (53.5 %) with the water solubility index and water absorption index of banana powder was determined at value of 56.98 % and 5.17 g/g, respectively. Foam mat dried banana powder was well preserved in paper packaging with aluminum foil.