Food Demand Research Articles

Asymmetric effects of planting pattern and density on leaf-height traits of Glycine max and Amaranthus retroflexus

Abstract Environmentally sustainable weed management is crucial to meet the increasing demand for food. Previous studies have demonstrated that improving spatial uniformity and planting density can effectively suppress weeds. However, research on how planting patterns (PPs) affect the functional traits of crops and weeds is limited. In 2019 and 2021, we conducted a field experiment to compare the functional traits and biomass of Glycine max and Amaranthus retroflexus in two PPs—row (R) and equidistant (E)—with varying combinations of G. max and A. retroflexus densities. We found that the EPP amplified the competitive ability of G. max in size-asymmetric competition with A. retroflexus, and this advantage increased alongside higher G. max density, primarily due to functional traits related to light acquisition. In the EPP, G. max established a closed canopy during the early growth stage, reducing light availability to A. retroflexus. This advantage was reflected in higher leaf area index (LAI) and leaf dry weight (LDW) for G. max in the EPP than in the RPP, while A. retroflexus experienced reduced LAI and plant height (PH) due to increased shading in the EPP. Consequently, the EPP enhanced the total biomass and yield of G. max by an average of 40.81% and 37.72%, respectively, while the biomass of A. retroflexus decreased by an average of 34.52% compared to the RPP. These results suggest that adopting an EPP with a high density of G. max, could be an effective strategy for suppressing A. retroflexus and improving crop yield.

Organic Farming in India: A Dual Strategy for Climate Change Adaptation and Mitigation

Recent decades have transformed Indian agriculture from traditional methods to a mechanized system reliant on fossil fuels, chemical fertilizers, and pesticides, increasing greenhouse gas emissions and impacting global climate. Higher greenhouse gas levels, including carbon dioxide, methane, and ozone are likely, causing the observed rise in air temperatures and leading to significant climate shifts. In most subtropical regions, a 4°C rise in global temperatures is anticipated to reduce groundwater and surface water, heightening food demand and threatening global food security. Climate change denotes long-term, significant alterations in climate measurements. A production system that maintains the health of ecosystems, soils, and human populations is known as organic farming (IFOAM 2006). Organic farming is energy-efficient and reduces greenhouse gas emissions by minimizing chemical and fossil fuel use, while enhancing soil carbon, biodiversity and fertility. It reuses plant and animal waste to restore soil nutrients, relies on renewable resources and supports sustainable, low-pollution management. Organic farming not only helps to mitigate climate change but also provides lasting benefits as an adaptation strategy. Effective nutrient management and carbon sequestration in soils are crucial for adapting and mitigating climate change across diverse temperature zones and local conditions.

Based on STM32 SCM Intelligent Greenhouse

With the continuous growth of the global population and the continuous development of agricultural production, there are many problems in the traditional agricultural production mode, such as resource waste, low production efficiency, environmental pollution, and the urgent need for innovative solutions to meet the growing food demand and sustainable development goals. This paper introduces an intelligent greenhouse system based on STM 32 single-chip microcomputer, using advanced technology and automation functions, in improving the efficiency, quality and sustainability of agricultural production. The hardware composition of the intelligent greenhouse system includes STM 32 MCU, various sensor actuators, ESP8266 module and user interface module.

Comparative Analysis of Machine Learning Models for Predicting Rice Yield: Insights from Agricultural Inputs and Practices in Rwanda

Food security is a global challenge, especially in developing countries like Rwanda. With a growing population and limited agricultural land, Rwanda struggles to meet increasing food demands. Rice is a staple food crop in Rwanda, playing a crucial role in the country’s food security. However, factors like climate variability, soil nutrient management, and limited access to high-quality inputs hinder rice yield optimization. This paper investigates the most effective machine learning model for predicting rice crop yield in Rwanda, using agricultural inputs and practices. The study used secondary datasets from the National Institute of Statistics of Rwanda (NISR) for rice yield prediction. Eight supervised machine learning algorithms were used, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, eXtreme Gradient Boosting Tree, and AdaBoost. The models were evaluated based on their accuracy in predicting rice yields, with RMSE, MAE, and Relative Error as primary metrics. The feature importance analysis was also conducted to identify significant factors influencing yield predictions. The study’s findings revealed that the Adaptive Boosting Tree model outperformed the other machine learning models in predicting rice yield. This model achieved RMSE, MAE and Relative Error of 0.69, 0.46 and 12.4%, respectively, indicating a high level of predictive accuracy. The feature importance analysis further highlighted the key factors that contributed to rice yield predictions, with the quantity of inorganic fertilizer, degree of erosion, season, and seed type emerging as the most influential variables. The study demonstrates the effectiveness of machine learning models, particularly the Adaptive Boosting Tree, in improving rice yield predictions and highlighting the crucial role of agricultural inputs like fertilizer and seed type, in influencing crop yields. The output from this study will help the farmers and stakeholders to make data-driven decisions about resource use and crop management.

Selection of tomato genotypes for organic production systems

ABSTRACT The development of tomato (Solanum lycopersicum L.) varieties adapted to organic production systems is crucial for meeting the demand for organic food and for supporting the sustainable agricultural sector. This study aimed to rescue, evaluate genetic diversity and select tomato genotypes suitable for growing under organic production conditions. To achieve this, the work involved assessing nine table tomato genotypes from different varietal groups in two environments (greenhouse and open field organic conditions). Morpho-agronomic and physical-chemical quality characteristics were evaluated. The experimental design used was a randomised block design with four replications. The data were subjected to analysis of variance, means tests, and Spearman’s correlations. Furthermore, hierarchical dendrograms and principal component analysis were performed to assess morphological variability between genotypes in each of the environments. Dissimilarity dendrograms revealed different clustering patterns under greenhouse and field conditions. In general, the genotypes grown in the greenhouse showed higher commercial yields and organoleptic quality, as well as an earlier crop cycle. The Jumbo, Santa Clara and Kada Gigante genotypes exhibited characteristics that were considered to make them suitable for organic cultivation in both environments, especially in the open field. Gaúcho Marmande presented characteristics (vegetative and physico-chemical) that made it suitable for the open field, while Coração-de-Boi, Bocaina and Roquesso were recommended only for greenhouse cultivation.

Bioengineering Bacillus spp. for Sustainable Crop Production: Recent Advances and Resources for Biotechnological Applications

AbstractThe goal of sustainable agriculture is to meet the rising need for food, while minimizing adverse impacts on the environment, protecting natural resources, and ensuring agricultural output over the long term. The pressing need to increase agricultural yield through sustainable agriculture is being emphasized. Several Bacillus species have been used as commercial biopesticides since they can act against plant pathogens by potentially suppressing them. At the same time, they can act as plant growth-promoting rhizobacteria and are known for their diverse characteristics and beneficial properties, making them potential candidates for use sustainable crop production programs. Knowledge of genetic information opens the door of possibility for understanding the way these microorganisms behave. By applying biotechnological tools to Bacillus, strategies can be adopted for the purpose of increasing the yield of crops and managing pests and pathogens that infect them. In this review, we identify the genes in the most significant Bacillus spp. that contribute to plant improvement. The most important biotechnological tools and advance computational approaches are described to provide an extended vision on this topic. However, increasing the crop production through application of beneficial microbial strains requires a multifaceted approach that considers ecological, economic, and social aspects. By implementing these strategies and practices, we can work towards a sustainable and resilient agricultural system that meets the growing food demand, while preserving the environment for future generations.

Variability of drinking water for pigs and poutry the southern region of Brazil over a twelve – month period

The increase in global food demand has led to intensified production of pigs and poultry. In this context, water quality on farms needs to be monitored to ensure maximum production and animal welfare. This study aimed to evaluate the quality and variability of drinking water for animals over twelve months in properties in the Southern Region of Brazil. Physical, chemical, microbiological parameters, and the average rainfall index of nine water sources were observed. Variations in pH were noted among the evaluated water sources, as well as within the same source over monthly collections. A relationship between average rainfall and elevated iron levels was also demonstrated. Nitrate and nitrite levels exceeded Brazilian legislation at certain sampling points, posing risks to human and animal health. In 67.59% of samplings, total coliforms were present, and 59.26% showed the presence of Escherichia coli. The study highlighted the variability of water sources in the Southern Region of Brazil, reflecting the need for constant monitoring and treatment measures, such as water treatment stations, chlorination systems, and acidification in rural properties.

Glyphosate contact alters the expression of genes in the head of africanized Apis mellifera bees

The increase in the world population results in a greater demand for food, intensifying the dependence on pesticides use. Glyphosate, a pesticide widely used in crops, can contaminate resources collected by bees. In this context, this study aimed to analyze the transcriptome of the head of Africanized Apis mellifera bees during the foraging phase to determine whether contact with the herbicide glyphosate, in lethal and sublethal doses, causes changes in the gene expression profile. For this, 180 marked bees were collected from their hives, aged 21 days, and distributed into three treatment groups: 1) bees in contact with the lethal dose of glyphosate (255.73 µg/bee); 2) bees in contact with the sublethal dose of glyphosate (2.5573 µg/bee); 3) control group with distilled water. Transcriptome analysis was performed on 12 bees from each treatment after one and four hours of exposure. The results show that one hour of contact with glyphosate resulted in 57 genes presenting differential expression in relation to the control (11 lethal and 53 sublethal), interfering in the signaling, communication and metabolism processes, while four hours of exposure, 38 genes (32 lethal and 11 sublethal) related, mainly, to the stimulation of the immune response. In summary, glyphosate affects the gene expression of honey bees during the foraging phase, at both doses, resulting in negative effects on behavior, metabolism and immune system, which compromises the health, activity and development of the colonies, also compromising the process of pollination.

Exploring Artificial Intelligence and Machine Learning in Precision Agriculture: A Pathway to Improved Efficiency and Economic Outcomes in Crop Production

This review analyzes secondary data from academic databases, research articles, and case studies to explore the role of new technologies for precision agriculture (PA) and investigates the value addition that Artificial Intelligence (AI) and Machine Learning (ML) provide to resource use, crop yield, and economic performance. Accordingly, the most of the key applications of AI in PA were related to crop yield prediction, disease detection, and effective water usage. Operating models through AI will analyze much data in real time, thus providing insight into informed decision making by farmers for proactive action against crop challenges like drought or pest attack. Furthermore, IoT devices and remote sensing support continuous monitoring in the delivery of correct data about optimizations of resources with minimal environmental impact. AI-driven robotics further automates all tasks related to planting, harvesting, and pesticide application, improving labor productivity and operational efficiency. This would involve in other issues like implementation costs, data privacy, and general unawareness among farmers of developing areas. Equally important will be ethical issues like ownership of data and loss of jobs. Various case studies in India, China, the United States, and Africa reveal how AI could transform the future of agriculture if integrated into agricultural systems properly to gain higher productivity and sustainability. Improvements in data quality and ethical issues, and increased access by smallholder farmers, will also be part of future research. Eventually, integrating AI with IoT, robotics, and big data analytics could provide high potential to meet global food demand in a sustainable manner.

Climate data integration into wheat performance evaluation reveals large inter-varietal responses

Abstract There is an urgent need to adapt crop breeding strategies to boost resilience in the face of a growing food demand and a changing climate. Achieving this requires an understanding of how weather and climate variability impacts crop growth and development. Using the United Kingdom (UK) as an example, we evaluate changes in the UK agroclimate and analyse how these have influenced domestic wheat production. Here we quantify spatial and temporal variability and changes in weather and climate across growing seasons over the last four decades (1981-2020). Drawing on variety trial data, we then use statistical modelling to explore the interaction between genotype and agroclimate variation. 
We show that changes in the UK agroclimate present both risks, and opportunities for wheat growers, depending on location. From 1981-2020, in Wales, the West Midlands, large parts of the North West, and Northern Ireland, there was an overall increase in frost risk in early spring of 0.15 additional frost days per year, whilst in the East early frost risk decreased by up to 0.29 days per year. Meanwhile, over the period 1987-2020, surface incoming shortwave radiation during grainfill increased in the East by up to 13% but decreased in Western areas by up to 15%. We show significant inter-varietal differences in yield responses to growing degree days, heavy rainfall and the occurrence of late frost. This highlights the importance of evaluating variety-climate interactions in variety trial analyses, and in climate-optimised selection of crops and varieties by growers. This work provides guidance for future research on how climate change is affecting the UK agroclimate and resulting impacts on winter cereal production.

An Overview of Malaysia’s Retail Organic Foods Market and Its Consumption Behavior

Growing global awareness of health and environmental sustainability has significantly increased the demand for organic foods, a trend gaining traction in Malaysia. This research provides a comprehensive overview of consumer consumption patterns and purchase intentions for organic foods in Malaysia’s retail sector, identifying key factors influencing their choices. The study examines Malaysia’s broader food retail landscape, focusing on the growing organic segment and its evolving consumption patterns. It also explores the organic food market, considering domestic production and the availability of imported goods that shape consumer choices and market growth. The study offers practical insights into consumer decision-making within the retail food market by examining consumption behavior, such as purchase intentions. The review uncovers critical insights into the organic food market, including its size, rising demand and consumption trends, government and retailer initiatives, and the demographic profiles of consumers who purchase or intend to purchase organic foods. These findings contribute to existing research on organic food consumption in Malaysia and provide valuable guidance for policymakers, government agencies, and retailers aiming to enhance the adoption of organic foods in the Malaysian retail sector.

Optimizing sunflower yield: Understanding pollinator contribution to inform agri-environmental strategies

ContextThe agricultural intensification due to global increased food demand has harmed pollinator communities worldwide. However, some of the economically most important oilseed crops, such as the sunflower, depend on pollinators to produce seeds. While self-fertile varieties have undergone genetic selection to guarantee productivity, the pollinator-dependence levels and the economic contribution of pollinators have not been fully estimated. ObjectiveHere, we aimed to explore floral and pollinator constraints limiting the agricultural yield of sunflower varieties most frequently used in the Iberian Peninsula. MethodsPollination experiments were undertaken to analyse the pollinator-dependence level of 12 varieties under controlled conditions and also under natural conditions in 23 fields of two Spanish agricultural regions. The selfing ability and economic contribution of pollinators were estimated by comparing bagged and open-pollination treatments. ResultsOur results showed that the degree of pollinator-dependence is highly dependent on sunflower variety, with impacts on production and productivity outcomes, e.g. individual plant yield values varied between 0.188 and 0.692. Several varieties could self-fertilize and produce seeds regardless of pollinators. However, outcrossing significantly increased seed set in most varieties with increments up to 0.341. Overall, a trade-off between the number and weight of seeds was observed. Under natural field conditions, pollinators significantly increased overall sunflower production, although differences were observed among regions (increment of 275 kg/ha in Burgos and 382 kg/ha in Cuenca), with an associated economic outcome. ConclusionsThe self-fertilization ability and the level of pollinator-dependence vary according to the intrinsic reproductive traits of the variety analysed. Although some varieties are able to produce seeds despite the absence of pollinators, the sunflower clearly benefits from insect pollination. The landscape context and the availability of pollinator communities influenced the final crop yield and the economic outcome. SignificanceCombining landscape-restoring interventions with the cultivation of self-compatible varieties during at least the first years of implementation may be a solid additional agri-environmental strategy to maintain production levels and economic outcomes, which may particularly mitigate effects of pollinator and biodiversity losses mainly in highly simplified agroecosystems.

Emergy-based evaluation of production efficiency and sustainability of diversified multi-cropping systems in the Yangtze River Basin

Excessive agricultural investment brought about by increased multiple-cropping index may compromise environmental sustainability. There are few studies on the sustainability of diversified multi-cropping systems in the Yangtze River Basin (YRB). Therefore, this study selected five representative locations in the YRB. According to the local climate characteristics and food demand, diversified multi-cropping systems were designed, and the main local winter crops were selected as the previous crops of the corn–soybean strip compound cropping system, with the local traditional double-cropping model as the control (CK). The emergy evaluation method was introduced to quantitatively compare the efficiency and sustainability of diversified multi-cropping systems in the YRB. The results showed that by incorporating soybean by intercropping with corn, compared with the CK, the total energy input, annual energy output, and annual economic output increased by 15.80%, 9.78%, and 33.12% on average, respectively. The unit emergy value (UEV) and unit non-renewable value (UNV) increased by 6.03% and 5.98%, respectively; the emergy yield ratio (EYR) and environmental loading ratio (ELR) decreased by 0.91% and 0.44%, respectively; the emergy sustainability index (ESI) was the same. In the third mature crop selection, compared with that of corn, the ELR of soybean decreased by 14.32%, and the ESI increased by 18.55%. In addition, the choice of winter crops plays a vital role in the system’s efficiency and sustainability. Compared with those of other winter crops, the annual economic outputs of potato (upper reaches of the YRB), potato or forage rape (middle reaches of the YRB), and wheat (lower reaches of the YRB) increased by 51.02%, 32.27%, and 0.94%, respectively; their ESI increased by 71.21%, 47.72%, and 12.07%, respectively. Potato–corn/soybean or potato/corn/soybean (upper reaches of the YRB), forage rape–corn/soybean or potato/corn/soybean (middle reaches of the YRB), and wheat–corn/soybean (lower reaches of the YRB) were chosen to facilitate the coexistence of high economic benefits and environmental sustainability. Additionally, promoting mechanization and reducing labor input were essential to improve the efficiency and sustainability of multi-cropping systems. This study would provide a scientific basis and theoretical support for the development of efficient and sustainable multiple-cropping systems in the dryland of the YRB.

Multispectral UAV-Based Disease Identification Using Vegetation Indices for Maize Hybrids

In the future, the cultivation of maize will become more and more prominent. As the world’s demand for food and animal feeding increases, remote sensing technologies (RS technologies), especially unmanned aerial vehicles (UAVs), are developing more and more, and the usability of the cameras (Multispectral-MS) installed on them is increasing, especially for plant disease detection and severity observations. In the present research, two different maize hybrids, P9025 and sweet corn Dessert R78 (CS hybrid), were employed. Four different treatments were performed with three different doses (low, medium, and high dosage) of infection with corn smut fungus (Ustilago maydis [DC] Corda). The fields were monitored two times after the inoculation—20 DAI (days after inoculation) and 27 DAI. The orthomosaics were created in WebODM 2.5.2 software and the study included five vegetation indices (NDVI [Normalized Difference Vegetation Index], GNDVI [Green Normalized Difference Vegetation Index], NDRE [Normalized Difference Red Edge], LCI [Leaf Chlorophyll Index] and ENDVI [Enhanced Normalized Difference Vegetation Index]) with further analysis in QGIS. The gathered data were analyzed using R-based Jamovi 2.6.13 software with different statistical methods. In the case of the sweet maize hybrid, we obtained promising results, as follows: the NDVI values of CS 0 were significantly higher than the high-dosed infection CS 10.000 with a mean difference of 0.05422 *** and a p value of 4.43 × 10−5 value, suggesting differences in all of the levels of infection. Furthermore, we investigated the correlations of the vegetation indices (VI) for the Dessert R78, where NDVI and GNDVI showed high correlations. NDVI had a strong correlation with GNDVI (r = 0.83), a medium correlation with LCI (r = 0.56) and a weak correlation with NDRE (r = 0.419). There was also a strong correlation between LCI and GNDVI, with r = 0.836. NDRE and GNDVI indices had the correlation coefficients with a CCoeff. of r = 0.716. For hybrid separation analyses, useful results were obtained for NDVI and ENDVI as well.

Research on time-varying path optimization for multi-vehicle type fresh food logistics distribution considering energy consumption

With the increasing demand for fresh food markets, refrigerated transportation has become an essential component of logistics operations. Currently, fresh food transportation frequently faces issues of high energy consumption and high costs, which are inconsistent with the development needs of the modern logistics industry. This paper addresses the optimization problem of multi-vehicle type fresh food distribution under time-varying conditions. It comprehensively considers the changes in road congestion at different times and the quality degradation characteristics of fresh goods during distribution. The objectives include transportation cost, dual carbon cost, and damage cost, subject to constraints such as delivery time windows and vehicle capacity. A piecewise function is used to depict vehicle speeds, proposing a dynamic urban fresh food logistics vehicle routing optimization method. Given the NP-hard nature of the problem, a hybrid Tabu Search (TS) and Genetic Algorithm (GA) approach is designed to compute an optimal solution. Comparison with TS and GA algorithm results shows that the TS-GA algorithm provides the best optimization efficiency and effectiveness for solving large-scale distribution problems. The results indicate that using the TS-GA algorithm to optimize a distribution network with one distribution center and 30 delivery points resulted in a total cost of CNY 12,934.02 and a convergence time of 16.3 s. For problems involving multiple vehicle types and multiple delivery points, the TS-GA algorithm reduces the overall cost by 2.94–7.68% compared to traditional genetic algorithms, demonstrating superior performance in addressing multi-vehicle, multi-point delivery challenges.

Nutritional quality of proteins from two beef co-products as determined in the growing pig.

The increasing demand for food and especially proteins leads to the search for alternative protein sources. Meat co-products, which are available but little used in human food, provide a potential solution to this challenge. The present study aimed to evaluate the nutritional quality of two beef protein ingredients (greasy greaves recovered proteins (GGRP) and water recovered proteins (WRP)), both co-products of the fat rendering process. Their true ileal digestibility (TID), digestible indispensable amino acid score (DIAAS) and kinetics of plasma amino acids (AA) were measured in ten growing pigs, each fed the two co-products and a protein-free diet. Titanium dioxide was used as an indigestible marker. Digesta samples were collected for 9 h after meal ingestion, and blood samples were collected at ten time points during the same period. Total nitrogen (N) and AA contents were determined. Data were statistically analysed using linear mixed models. The TID of total N was not different between WRP and GGRP (81-84 %, P > 0·05). The first-limiting AA was Trp for both ingredients, with a DIAAS much higher for GGRP than for WRP (74 and 10 % for adults, respectively; P < 0·001). Postprandial plasma AA concentration peaked earlier for WRP (3 h) than for GGRP (5 h). Plasma concentrations of total and essential AA were higher (P < 0·001) with GGRP diet than WRP diet. Overall, GGRP has a nutritional quality suitable to meet the needs of adults for AA, while WRP needs to be supplemented with other protein sources to fulfil the dietary requirements.

2023 Jishishan Earthquake-triggered river terrace landslide enabled by tectonic and human activities

The continued increase in demand for food is such that flood-irrigation-based agricultural techniques are not expected to change in the future. Consequently, in cultivated areas dominated by river terraces that experience neotectonic movements, catastrophes caused by earthquakes that trigger material liquefaction may continue to occur. Among such catastrophes, low-angle, ultra-long-distance landslides induced by synergistic effects of tectonic and human activities are events that are not fully understood but can potentially be mitigated. A landslide-mudflow that occurred on December 18, 2023, during an Ms 6.2 earthquake in Jishishan, China, on the tertiary terrace of the Yellow River. We reveal the causal mechanisms by which geomorphology, tectonics, and human activities controlled the landslide-mudflow, and outline a new, potentially widespread, dual liquefaction layer-dominated failure mode for earthquake-induced landslides on river terraces. Our findings serve as a warning that urgent landslide-potential assessments should be conducted across river terrace irrigation areas in tectonically active regions.

Utilization of Microencapsulated Polyphenols to Enhance the Bioactive Compound Content in Whole Grain Bread: Recipe Optimization

In today’s health-conscious society, there is an increasing consumer demand for functional foods that not only satisfy nutritional needs but also promote overall well-being. The aim of this study was to develop a bread formulation enriched with microencapsulated polyphenols, oat β-glucan concentrate, and sour fermented beetroot juice to enhance its nutritional profile and health benefits. To protect sensitive polyphenols from thermal degradation during baking, the microencapsulation technique was employed to maintain their bioactivity. The influence of these ingredients on the physio-chemical parameters of bread (dough viscosity, hardness, porosity, bioactive ingredients content, color, and volatile compounds profiles), as well as sensory acceptability, was evaluated. Using response surface methodology, the formulation was optimized to achieve a product with high polyphenol and β-glucan content. The optimized formulation included a content of 4.60% sour fermented beetroot juice, 6.29% β-glucan concentrate, and 2.77% microencapsulated polyphenols. The final bread demonstrated significant antioxidant activity and high consumer acceptability, indicating its potential as a functional alternative to traditional bread. This innovative approach addresses the demand for healthier food options and emphasizes the role of functional ingredients in improving dietary quality and promoting health benefits.

Respon Pertumbuhan dan Hasil Microgreens Beberapa Varietas Tanaman Caisim (Brassica juncea L.) pada Berbagai Jarak Tanam

The diminishing amount of land available for agriculture and the growing demand for nutritious food have made it necessary to find alternative sources of fresh vegetables. Urban farming, especially the cultivation of microgreens, can help meet this demand. Microgreens offer several advantages, such as time and cost savings, and they require minimal space while growing quickly. However, the optimal spacing for different types of microgreens is not extensively documented, as research on microgreens is still limited in Indonesia. The Brassicaceae family is one of the most commonly cultivated families for microgreens. This study was carried out at Room A, Faculty of Agriculture, Singaperbangsa Karawang University, Campus 2. The experimental method used was a factorial randomized block design (RBD Factorials) with two treatments for varieties and three for spacing, repeated five times, resulting in 30 experimental units. Factor I, Varieties, consisted of v1 (Tosakan) and v2 (Shinta). Factor II, Spacing, consisted of j1 (1 cm x 1 cm), j2 (1 cm x 2 cm), and j3 (2 cm x 2 cm). The data were analyzed using an analysis of variance (Uji F) at a 5% level. If the results of the analysis of variance were significantly different, the study continued with a multiple range test (DMRT) at a 5% level to determine the best-performing treatment. There was an interaction between the treatments for microgreen varieties and spacing on parameters such as plant density, dry weight of the plant, and fresh weight. There was a significant effect of the variety on the parameter of chlorophyll content, and a significant effect of spacing on the parameters of plant growth rate and the age of leaf appearance. Additional types of plants are needed to determine significant differences related to spacing and their interactions.

Leveraging genomic prediction to surpass current yield gains in spring barley

Key messageGenetic gain in Nordic spring barley varieties was estimated to 1.07% per year. Additionally, genomic predictive ability for yield was 0.61 in a population of breeding lines.Barley is one of the most important crops in Europe and meeting the growing demand for food and feed requires continuous increase in yield. Genomic prediction (GP) has the potential to be a cost-efficient tool in breeding for complex traits; however, the rate of yield improvement in current barley varieties is unknown. This study therefore investigated historical and current genetic gains in spring barley and how accounting for row-type population stratification in a breeding population influences GP results. The genetic gain in yield was estimated using historical data from field trials from 2014 to 2022, with 22–60 market varieties grown yearly. The genetic gain was estimated to 1.07% per year for all varieties, serving as a reference point for future breeding progress. To analyse the potential of using GP in spring barley a population of 375 breeding lines of two-row and six-row barley were tested in multi-environment trials in 2019–2022. The genetic diversity of the row-types was examined and used as a factor in the predictions, and the potential to predict untested locations using yield data from other locations was explored. This resulted in an overall predictive ability of 0.61 for yield (kg/ha), with 0.57 and 0.19 for the separate two-row and the six-row breeding lines, respectively. Together this displays the potential of implementing GP in breeding programs and the genetic gain in spring barley market varieties developed through GP will help in quantifying the benefit of GP over conventional breeding in the future.