Relationship between pre-slaughter stress responsiveness and beef quality in three cattle breeds

The relationship between stress responsiveness and beef quality of 40 Nguni, 30 Bonsmara and 30 Angus steers was determined. The L* values, pHu, cooking loss (CL) and Warner-Bratzler shear force (WBSF) were determined. Catecholamine levels were determined from urine samples collected at slaughter. Bonsmara steers had the highest (P \u3c 0.05) levels of catecholamines with respective epinephrine, norepinephrine and dopamine concentrations of 10.8, 9.7 and 14.8 nmol/mmol. Nguni steers had the lowest (P \u3c 0.05) levels of catecholamines, with respective catecholamine concentrations of 5.1, 4.3 and 4.0 nmol/mmol. In the Nguni steers, there were significant (P \u3c 0.05) correlations between catecholamines and L* and between dopamine and tenderness in meat aged for two days (WBSF2). In the Bonsmara, dopamine was correlated (P \u3c 0.05) pHu, WBSF2 and CL. No significant correlations were found in the Angus. Therefore the relationship between stress responsiveness and certain beef quality traits may not be similar in different breeds

Relationship between pre-slaughter stress responsiveness and beef quality in three cattle breeds

The relationship between stress responsiveness and beef quality of 40 Nguni, 30 Bonsmara and 30 Angus steers was determined. The L* values, pHu, cooking loss (CL) and Warner-Bratzler shear force (WBSF) were determined. Catecholamine levels were determined from urine samples collected at slaughter. Bonsmara steers had the highest (P \u3c 0.05) levels of catecholamines with respective epinephrine, norepinephrine and dopamine concentrations of 10.8, 9.7 and 14.8 nmol/mmol. Nguni steers had the lowest (P \u3c 0.05) levels of catecholamines, with respective catecholamine concentrations of 5.1, 4.3 and 4.0 nmol/mmol. In the Nguni steers, there were significant (P \u3c 0.05) correlations between catecholamines and L* and between dopamine and tenderness in meat aged for two days (WBSF2). In the Bonsmara, dopamine was correlated (P \u3c 0.05) pHu, WBSF2 and CL. No significant correlations were found in the Angus. Therefore the relationship between stress responsiveness and certain beef quality traits may not be similar in different breeds

Seed Performance of Selected Bottle Gourd (Lagenaria siceraria (Molina) Standl.)

Aims: Bottle gourd is a useful crop to include in climate change adaption strategies for agronomy. However, diversity in plant and seed forms makes it difficult to predict performance under field management. There is a dearth of knowledge on the relationship between seed morphology and seed performance, namely, germination and early establishment of seedlings. This led to a need to evaluate seed morphology of different bottle gourd landraces and its effect on seed quality as defined by germination and vigour. Methodology: Six mature fruits of different bottle gourd landraces were collected from subsistence farmers and seeds from each fruit were morphologically characterized. Standard germination test, root: shoot ratio, seedling fresh mass, seedling dry mass, germination velocity index (GVI) and electrical conductivity were used to establish seed quality and vigour. Results: Although all traits were significantly different, most of them were not good indicators of seed quality. Seed coat thickness isolated varieties by provenance and was inversely proportional to root: shoot ratio as a measure of seedling establishment. Conclusion: It is concluded that Lagenaria siceraria seed morphology could be a useful trait for selection of planting material in the context of seed germination as a trait

Neglected and underutilised crops: a systematic review of their potential as food and herbal medicinal crops in South Africa

The African continent harbours many native species with nutraceutical and pharmaceutical potential. This study reviewed underutilised crops in South Africa to determine their potential as food and herbal medicinal crops. Over 5,000 species have been identified and earmarked for their medical attributes in formal and informal setups. Researchers, plant breeders and policymakers have mostly ignored the development potential of these crops. Consequently, their value chains are poorly developed. In South Africa, there is a wide range of neglected and underutilised crops, which were historically popular and used by communities; however, over the years, they have lost their status within farming systems and been relegated to the status of neglected and underutilised. Recently, driven by the need to transition to more sustainable and resilient food systems, there has been renewed interest in their potential as food and herbal medicinal crops to establish new value chains that include vulnerable groups. They are now gaining global attention, and their conservation and sustainable utilisation are now being prioritized. The review confirmed that several of these crops possess nutraceutical and pharmaceutical properties, highlighting their potential for development as food and herbal medicines. However, current production levels are too low to meet the requirements for industrial development; research and development should focus on all aspects of their value chain, from crop improvement to utilisation. A transdisciplinary approach involving a wide range of actors is needed to develop the identified neglected and underutilised crops’ potential as food and herbal medicinal crops and support the development of new and inclusive value chains

A systematic review of UAV applications for mapping neglected and underutilised crop species’ spatial distribution and health

Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South

Options for improving agricultural water productivity under increasing water scarcity in South Africa

South Africa is ranked among the thirty driest countries in the world, a challenge that is negatively affecting agricultural production. Other challenges such as population growth, rural-urban migration, changing food preferences and drought exacerbate pressure on agricultural water productivity. The review critically assessed the different considerations for increasing agricultural water productivity under water scarce conditions in South Africa. While under these conditions, irrigation may seem an obvious solution to increasing agricultural water productivity as a response to frequent droughts and mid-season dry spells. However, considerations on the availability of water and energy for irrigation expansion and the accessibility of irrigation services to different farming groups in the country. It is generally argued that irrigation is an expensive option and not necessarily readily accessible to most farmers. There are prospects for tapping into South Africa’s groundwater resources but the extent to which they can contribute to expanding area under irrigation is contested given the challenges of quantifying and pumping the water. Most smallholder farmers currently lack access to water, energy, infrastructure and technical skills to irrigate thus making irrigation a challenging option in this sector. An alternative would be to explore rainwater harvesting and soil water conservation technologies, which involve inducing, collecting, storing and conserving runoff water for agriculture. The drawbacks to this are that, apart from scale issues, rainfall is becoming more erratic and droughts more frequent and hence the feasibility of this approach under frequent drought could be challenged

Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season

Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity

Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules

Diversity and diversification: ecosystem services derived from underutilized crops and their co-benefits for sustainable agricultural landscapes and resilient food systems in Africa

There are growing calls to adopt more sustainable forms of agriculture that balance the need to increase production with environmental, human health, and wellbeing concerns. Part of this conversation has included a debate on promoting and mainstreaming neglected and underutilized crop species (NUS) because they represent a more ecologically friendly type of agriculture. We conducted a systematic review to determine the ecosystem services derived from NUS and assess their potential to promote functional ecological diversity, food and nutritional security, and transition to more equitable, inclusive, sustainable and resilient agricultural landscapes and food systems in Africa. Our literature search yielded 35 articles for further analysis. The review showed that NUS provide various provisioning, regulating, cultural, and supporting ecosystem services and several environmental and health co-benefits, dietary diversity, income, sustainable livelihood outcomes, and economic empowerment, especially for women. Importantly, NUS address the three pillars of sustainable development- ecological, social, and economic. Thus, NUS may provide a sustainable, fit-for-purpose transformative ecosystem-based adaptation solution for Africa to transition to more sustainable, healthy, equitable, and resilient agricultural landscapes and food systems

Modelling neglected and underutilised crops: a systematic review of progress, challenges, and opportunities

Developing and promoting neglected and underutilised crops (NUS) is essential to building resilience and strengthening food systems. However, a lack of robust, reliable, and scalable evidence impedes the mainstreaming of NUS into policies and strategies to improve food and nutrition security. Well-calibrated and validated crop models can be useful in closing the gap by generating evidence at several spatiotemporal scales needed to inform policy and practice. We, therefore, assessed progress, opportunities, and challenges for modelling NUS using a systematic review. While several models have been calibrated for a range of NUS, few models have been applied to evaluate the growth, yield, and resource use efficiencies of NUS. The low progress in modelling NUS is due, in part, to the vast diversity found within NUS that available models cannot adequately capture. A general lack of research compounds this focus on modelling NUS, which is made even more difficult by a deficiency of robust and accurate ecophysiological data needed to parameterise crop models. Furthermore, opportunities exist for advancing crop model databases and knowledge by tapping into big data and machine learning