ABSTRACT: The inactivated whole soy (IWS) was studied in pigs to determine the energy value through a metabolism trial and evaluate the effect of IWS and protease on performance, carcass traits, and economic viability. Metabolism with pigs (53.80±4.15kg) to determine digestibility coefficients of dry matter (DM), organic matter (OM), and crude protein (CP), digestible energy (DE), apparent metabolizable energy (AME), and nitrogen-corrected AME (AMEn). For the performance trial, 60 immunocastrated male pigs and 60 female pigs (30.09±1.46kg) were used in a 2 (gender) x 2 (with and without IWS) x 2 (protease) factorial arrangement. The DM, OM, and CP metabolizability coefficients of IWS were 83.77, 84.43, and 89.18%, respectively. The DE, AME, and AMEn values were 4904±117, 4805±273, and 4656±255kcal/kg, respectively. In growth phase I, enzyme provided an increase in average daily feed intake. In the economic viability, diet with IWS and without the enzyme had the lowest cost per kilogram of weight gained and provided the highest economic efficiency index and net revenue. The IWS has a high energy value and when used in diets for growing and finishing pigs provides satisfactory performance and better economic efficiency. RESUMO: A soja integral desativada (SID) foi estudada em suínos para determinar o valor energético, por meio de um ensaio de metabolismo, e avaliar o efeito da SID e da protease no desempenho, nas características da carcaça e na viabilidade econômica. Foi realizado um ensaio de metabolismo com suínos (53,80±4,15kg) para determinar os coeficientes de digestibilidade da matéria seca (MS), da matéria orgânica (MO) e da proteína bruta (PB), a energia digestível (ED), a energia metabolizável aparente (EMA) e a EMA corrigida pelo nitrogênio (EMAn). Para o ensaio de desempenho, foram utilizados 60 suínos machos imunocastrados e 60 suínos fêmeas (30,09 ± 1,46kg), em arranjo fatorial 2 (sexo) x 2 (com e sem SID) x 2 (protease). Os coeficientes de metabolizabilidade da MS, MO e PB do SID foram de 83,77, 84,43 e 89,18%, respectivamente. Os valores de ED, AME, e AMEn foram 4904 ± 117, 4805±273, e 4656±255kcal/kg, respectivamente. Para o desempenho na fase de crescimento I, a enzima proporcionou um aumento no consumo médio diário de ração. Em termos de viabilidade econômica, a dieta com SID e sem a enzima teve o menor custo por quilograma de peso ganho e proporcionou o maior índice de eficiência econômica e receita líquida. A SID possui um alto valor de energia e, quando usada em dietas para suínos em crescimento e terminação, proporciona um desempenho satisfatório e uma melhor eficiência econômica.

ABSTRACT The inactivated whole soy (IWS) was studied in pigs to determine the energy value through a metabolism trial and evaluate the effect of IWS and protease on performance, carcass traits, and economic viability. Metabolism with pigs (53.80±4.15kg) to determine digestibility coefficients of dry matter (DM), organic matter (OM), and crude protein (CP), digestible energy (DE), apparent metabolizable energy (AME), and nitrogen-corrected AME (AMEn). For the performance trial, 60 immunocastrated male pigs and 60 female pigs (30.09±1.46kg) were used in a 2 (gender) x 2 (with and without IWS) x 2 (protease) factorial arrangement. The DM, OM, and CP metabolizability coefficients of IWS were 83.77, 84.43, and 89.18%, respectively. The DE, AME, and AMEn values were 4904±117, 4805±273, and 4656±255kcal/kg, respectively. In growth phase I, enzyme provided an increase in average daily feed intake. In the economic viability, diet with IWS and without the enzyme had the lowest cost per kilogram of weight gained and provided the highest economic efficiency index and net revenue. The IWS has a high energy value and when used in diets for growing and finishing pigs provides satisfactory performance and better economic efficiency.

Infestation with insects disqualifies fresh spice herbs for local and export markets, severely affecting revenue and the grower’s reputation. An innovative positive pressure walk-in tunnel, in which air is drawn and filtered via insect screens into an enclosed, pressurized growing structure was designed and tested as a solution for mitigating crop infestation. In four field trials between 2019 and 2024 with sweet basil (Ocimum basilicum), the efficacy of the positive pressure tunnel was tested and compared with the current commercially used passive, pesticide-sprayed tunnel. Insect counts were reduced by 81%. Yields were 55% higher than in the passive control, and basil downy mildew (BDM), caused by Peronospora belbahrii, was inhibited in two of three trials in the pressurized system without pesticides. Over the 4-year study, chemical pesticide use was reduced by 88% and the number of basil harvests increased by 33%. An economic viability for sweet basil calculated the positive pressure tunnel to be $22,000 USD/hectare more profitable than the currently used passive tunnels. The research verified that the positive pressure tunnel was effective in reducing insect penetration and minimizing pesticide applications. Growers incorporating this innovative apparatus are expected to produce crops with reduced pesticide exposure or pesticide-free, appealing to customers’ desire for high-quality produce.

Aspergillosis is a fungal disease caused by the inhalation of <i>Aspergillus</i> spores, with <i>Aspergillus fumigatus</i> being the primary causative agent. This thermotolerant fungus affects both immunocompetent and immunocompromised individuals, posing a significant public health concern. In recent years, the detection of <i>A. fumigatus</i> in food products and production environments has raised questions about its potential role as an additional route of exposure. Furthermore, the emergence of azole-resistant strains in agricultural settings highlights the need to better understand its transmission dynamics and implications for food safety. This review explores the occurrence of <i>A. fumigatus</i> in crops and food products, its possible routes of contamination, and the potential link between environmental exposure to azole fungicides and resistance development. Additionally, it identifies knowledge gaps and proposes future research directions to improve risk assessment and mitigation strategies within the food production chain.

Calcium (Ca) is necessary for bone health and metabolic functions in poultry, however, the extent to which it can be utilized varies among feed ingredients. The goal of this study was to determine the apparent ileal digestibility (AID) and standardized ileal digestibility (SID) of calcium in wheat and soybean meal (SBM) in young quail chicks using a direct method. Three dietary treatments were used in the experiment: a calcium-free basal diet to determine endogenous calcium losses, and two diets with either wheat or SBM as the primary calcium sources. Titanium dioxide was incorporated as an indigestible marker for precise measurement of ileal digestibility. On day 30, a total of 300 male quail chicks were weighed and then randomly assigned to one of the three treatments. Each treatment comprised five replicate pens, with 20 birds per pen. On day 34, the birds were euthanized using carbon dioxide, and ileal digesta samples were collected from the distal two-thirds of the ileum to analyze Ca content. The endogenous ileal Ca loss was determined to be 179 ± 68.6 mg/kg of dry matter intake (DMI), providing a baseline for calculating SID values. The AID of calcium for wheat grain was determined to be 24.3 %, with the SID measuring 31.2 %. However, SBM had higher Ca digestibility, with AID and SID values at 34.6 % and 39.9 %, respectively. Understanding digestibility metrics is crucial for optimizing dietary formulations to ensure adequate Ca intake, particularly when plant-derived sources of Ca are used.

Twenty maize genotypes including breeding lines along with improved local released varieties were assessed for grain yield and yield component stability under three different maize growing agro-ecological zones from January to August 2023. A randomized complete block design (RCBD) with three replication was employed. additive main effects and multiplicative interaction (AMMI) and genotype-by-environment interaction (GGE) analysis was utilized to study the genotype and environment (GE) interactions. The combined analysis of variance showed a significant difference (p ≤ 0.001) in grain yield traits between maize genotypes, environments, interactions and IPC components. The average mean grain yield ranged from 3244 to 5103 kg/ha with a grand mean of 4111 kg/ha The maximum and minimum grain yield was 12325.93 kg/ha (G4) and 1159.68 kg/ha (G9) respectively. The biplot results showed genotype G20, G3, G1, G17 and G15 demonstrated consistently high stable grain yield performance. Genotypes G14, G19 and G4 had high yields, but were unstable as they were far from the IPC1 zero line. Multi-trait stability value indicated that the best genotypes in performance for all traits were G5, G17 and G19. AMMI analysis revealed that the environment at Kibaigwa and NM-AIST was the most ideal environment for seed yield traits. Based on the consistently high and stable grain yield performance, along with other superior grain qualities and plant characteristics, genotypes G5 and G17 were selected and recommended to be advanced and released as new commercial maize varieties in Tanzania for wider cultivation by farmers together with G19, a commercially released variety in Tanzania.

Accurate assessment of thermal comfort thresholds for livestock is essential for their health and production performance. However, determining appropriate thresholds is challenging due to complex interactions among climate conditions and animal characteristics, including species, breed, circadian rhythms, behavioral habits, and production stages. This study aimed to specify the thermal comfort thresholds of growing rabbits under high-temperature and high-humidity conditions. A 35-day summer experiment was conducted with 240 growing rabbits, monitoring environmental parameters such as air temperature (AT), relative humidity (RH), wind speed (WS), and black globe temperature, along with production data including voluntary feed intake, weekly body weight, and body temperature (BT). Methods such as time series decomposition, and singular spectrum analysis were employed to extract data features, isolate age effects, quantify the effects of AT and RH on the responses of rabbits at different ages. Results showed that AT and RH significantly impacted rabbit growth, with BT showing a lagged response to AT changes. After 48 days of age, rabbits preferred cooler AT, and the time lag of BT response to cooling (9–18 h) was longer than that to warming (2–13 h). Age-specific AT preferences were found (with AT > 24.5℃, RH > 80 %, WS <0.1 m/s persistently): rabbits preferred 26.1–28.3℃ at aged 35–43d, 43–49d preferred 25.5–27.7℃, and 49–56d preferred 24.7–25.6℃. After 56d, AT below 24℃ and RH below 80 % were optimal. This study provides a scientific basis for thermal environment management in rabbit production and offers a new perspective for assessing environmental welfare in other animals.

Sand and dust storms increasingly threaten global environmental and public health. To date, 150 countries are directly affected, with more than 100 classified as non-dust source regions. With climate change, these storms are expected to become more frequent and severe. Despite international awareness and initiatives, such as those led by the UN, crucial knowledge gaps continue to hinder effective, evidence-based public responses to sand and dust storms. In this Viewpoint, we review existing gaps in health research and highlight four key research priorities: the comprehensive health effects of sand and dust storms, including short-term and long-term exposures, diseases, regions, and health outcomes; the key particle sizes and toxic components of particles during sand and dust storms; the design of multicentre studies accounting for region-specific exposure patterns; and research on health outcomes attributable to particulate matter mixtures dominated by windblown dust versus other sources. We urgently call for international, collaborative, and multidisciplinary health studies considering sand and dust storm exposure characteristics and for the adoption of scientifically robust epidemiological methods in these studies. | This study was supported by the National Natural Science Foundation of China (82425051 and 82241051; TL), the National Key R&D Program of China (2023YFC3708302; QL), the Chinese Academy of Sciences Project for Young Scientists in Basic Research (YSBR-086; QL), and the National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (2024NITFID407; CZ and 2024NITFID601; TL). The views expressed in the article are those of the authors and do not necessarily reflect the views of the Health Effects Institute, or its sponsors. | Peer reviewed

Purpose: This paper presents a comprehensive bibliometric analysis of vermicomposting research over the past decade, focusing on its several advantages to sustainable agriculture and waste management. Method: The study encompasses 3259 papers published during the last decade (2013-2022), with a focus on the last five years to identify emerging trends. Results: A substantial growth in vermicomposting research is observed, with an annual publication output doubling from 2013 to 2022. Subject area analysis reveals prominence in Agricultural and Biological Sciences and Environmental Science. International collaboration has increased over the decade, with India leading in quantity but displaying a Field-Weighted Citation Impact (FWCI) below the global average. Other countries, including China, Spain, USA, and Malaysia, exhibit higher FWCI scores. Academic-corporate collaboration is limited, with only 9 papers resulting from such partnerships. Patent analysis indicates a growing interest in vermicomposting-related innovations, with 35 patents published since 2015, emphasizing sustainable practices in agriculture and waste management. Conclusion: Emerging trends encompass the development of methods for assessing vermicompost maturity and investigating its integration with other waste treatment technologies, optimization of vermicomposting through feedstock diversification, process fine-tuning, and innovative reactor designs, and environmental applications, involving the removal of heavy metals, degradation of microplastics, and wastewater treatment. Research Highlights • A bibliometric analysis was conducted using Scopus, VOSviewer and SciVal • A steady increase in annual publications with a particular uptick since 2019 • 3259 research articles and 35 patents were published during 2013-2022 • India is the most prolific country, followed by Iran and China • Emerging trends: innovative reactor designs, and new environmental applications

Abstract Background Cotton is one of the most important commercial crops after food crops, especially in countries like India, where it’s grown extensively under rainfed conditions. Because of its usage in multiple industries, such as textile, medicine, and automobile industries, it has greater commercial importance. The crop’s performance is greatly influenced by prevailing weather dynamics. As climate changes, assessing how weather changes affect crop performance is essential. Among various techniques that are available, crop models are the most effective and widely used tools for predicting yields. Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka, India, utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors. The artificial neural networks (ANNs) performed superiorly with acceptable yield deviations ranging within ± 10% during both vegetative stage (F1) and mid stage (F2) for cotton. The model evaluation metrics such as root mean square error (RMSE), normalized root mean square error (nRMSE), and modelling efficiency (EF) were also within the acceptance limits in most districts. Furthermore, the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district. Specifically, the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum temperature had a major influence on cotton yield in most of the yield predicted districts. These differences highlighted the differential interactions of weather factors in each district for cotton yield formation, highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka. Conclusions Compared with statistical models, machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield formation at different growth stages. This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield. Thus, the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.