Efficacy of fecal fertilizers on growth, nutrient uptake and yield of maize

Maize (Zea mays L.) is the most widely cultivated staple food crop in sub-Saharan Africa. However, its production is severely constrained by abiotic and biotic factors of which declining soil fertility is a major contributor. A study was conducted to evaluate the efficacy of fecal matter based organic fertilizers on growth, nutrient uptake, yield and yield components of maize, in two distinct agro-ecological zones. Five fertilizer treatments (control, Diammonium Phosphate (DAP), cow manure, struvite, compost from fecal matter) were tested in a randomized complete block design (RCBD) with four replications per site. Data were collected on crop emergence (%), plant height, number of leaves per plant, leaf area index (LAI), tasseling (%), nutrient uptake and grain yield and yield components. Data were subjected to analysis of variance and treatment means separated using Tukey’s HSD test. Results showed that crop emergence in the control treatment, except for struvite, was significantly higher than DAP and fecal matter based organic fertilizer plots in Bahati and Lanet sites. The end-point plant height (9 WAP), LAI and tasseling were significantly (P<0.05) influenced by location and organic fertilizer treatments. At the Lanet site, DAP and struvite treatments equally had the tallest maize plants (163 cm) followed by fecal compost (128 cm), manure (121 cm), and the control (79 cm). Similar result trends were recorded in Bahati where struvite (193 cm) had the tallest plants followed by fecal compost (166 cm), DAP (155 cm), manure (151 cm) and the control (98 cm), respectively. A contrasting result was observed at the Egerton University site in which cow manure and the control plots equally had the tallest plants (117-121 cm), followed by DAP and fecal compost (98-99 cm), and struvite (91 cm). The LAI, tasseling and grain yield were significantly influenced by location with struvite and fecal compost treatments producing the highest grain yield (≈8 t/ha) and one thousand (1000)-seed weights (480-560 g) at the Egerton University and Bahati experimental sites. Nitrogen uptake by maize for organic fertilizer treatments was higher than the control at all three locations. However, there was no difference in uptake of phosphorous and potassium between control and organic fertilizer treatments. These findings have demonstrated the potential of fecal matter based organic fertilizers as alternatives to inorganic fertilizers in smallholder agriculture.Key words: Zea mays, fecal compost, struvite, nutrient uptake, manure, Nitrogen, Phosphorus, Potassiu

Antifungal Activities of Some Medicinal Plants Against Colletotrichum lindemuthianum, the Causal Pathogen of Bean Anthracnose, and their Effect on Seed Germination and Seedling Performance

This research article was published by the Centre for Advancement of Applied Sciences in Volume : 24, Issue : 4, 2022This study evaluated the in vitro potency of Plectranthus barbatus, Vernonia amygdalina, Conyza bonariensis, Leonotis nepetifolia, and Lantana camara extracts against Colletotrichum lindemuthianum, the causal pathogen of anthracnose in common bean (Phaseolus vulgaris), as well as assessed their effect on seed germination and seedling performance under greenhouse conditions. In vitro assessment of antifungal activities of extracts was carried out using the poisoned food technique. Ethyl acetate extract of C. bonariensis was found most effective against the pathogen and completely inhibited its growth at 5.0, 2.5, and 1.25 mg mL-1 while it showed 91.2% inhibition at 0.5 mg mL-1 level. This was followed by P. barbatus and L. nepetifolia which completely inhibited the growth at 5.0 and 2.5 mg mL-1 level. Methanolic extracts were also effective with highest inhibition observed for L. camara (85.1%) at 5.0 mg mL-1, followed by P. barbatus (84.7%) and L. nepetifolia (83.1%) at the same concentration. Similarly, the aqueous extracts showed remarkable inhibition at the highest concentrations tested. Aqueous extracts of L. nepetifolia, V. amygdalina, and C. bonariensis inflicted maximum inhibition at 5.0 mg mL-1 (75.0, 74.7, and 73.3%, respectively). Extracts had no adverse effect on seed germination and seedling performance, but the test fungicide reduced seed germination significantly (p < 0.001). Easy accessibility of the studied medicinal plants and their potential in managing bean anthracnose provides an opportunity to use such plant extracts as seed dressers to manage bean anthracnose in smallholder farmers in Tanzania

Framing food security and food loss statistics for incisive supply chain improvement and knowledge transfer between Kenyan, Indian and United Kingdom food manufacturers

The application of global indices of nutrition and food sustainability in public health and the improvement of product profiles has facilitated effective actions that increase food security. In the research reported here we develop index measurements further so that they can be applied to food categories and be used by food processors and manufacturers for specific food supply chains. This research considers how they can be used to assess the sustainability of supply chain operations by stimulating more incisive food loss and waste reduction planning. The research demonstrates how an index driven approach focussed on improving both nutritional delivery and reducing food waste will result in improved food security and sustainability. Nutritional improvements are focussed on protein supply and reduction of food waste on supply chain losses and the methods are tested using the food systems of Kenya and India where the current research is being deployed. Innovative practices will emerge when nutritional improvement and waste reduction actions demonstrate market success, and this will result in the co-development of food manufacturing infrastructure and innovation programmes. The use of established indices of sustainability and security enable comparisons that encourage knowledge transfer and the establishment of cross-functional indices that quantify national food nutrition, security and sustainability. The research presented in this initial study is focussed on applying these indices to specific food supply chains for food processors and manufacturers

Assessment of heavy metals in sewage sludge and their accumulation in cabbage (Brassica oleracea var capitata)

Heavy metals accumulation in sewage sludge is a major concern to the environment especially when it is considered to be used for crop production. This research aimed at checking the levels of heavy metals in faecal matter based fertiliser products and if there is any subsequent absorption by cabbage when used in the field. Sewage sludge was used as major raw material to produce sludge, biochar, normal compost and vermi compost. Tests were done on the products and later on cabbage tissues. The results obtained showed that the products had no alarming levels of heavy metals as well as the levels in the tissues were not beyond the permissible levels. This indicates these products as safe for cabbage production

Detecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model

Detecting the flowering stage of tea chrysanthemum is a key mechanism of the selective chrysanthemum harvesting robot. However, under complex, unstructured scenarios, such as illumination variation, occlusion, and overlapping, detecting tea chrysanthemum at a specific flowering stage is a real challenge. This paper proposes a highly fused, lightweight detection model named the Fusion-YOLO (F-YOLO) model. First, cutout and mosaic input components are equipped, with which the fusion module can better understand the features of the chrysanthemum through slicing. In the backbone component, the Cross-Stage Partial DenseNet (CSPDenseNet) network is used as the main network, and feature fusion modules are added to maximize the gradient flow difference. Next, in the neck component, the Cross-Stage Partial ResNeXt (CSPResNeXt) network is taken as the main network to truncate the redundant gradient flow. Finally, in the head component, the multi-scale fusion network is adopted to aggregate the parameters of two different detection layers from different backbone layers. The results show that the F-YOLO model is superior to state-of-the-art technologies in terms of object detection, that this method can be deployed on a single mobile GPU, and that it will be one of key technologies to build a selective chrysanthemum harvesting robot system in the future

Weight and volume estimation of single and occluded tomatoes using machine vision

The fundamental characteristics of agricultural products are appearance, size, and weight, which affect their market value, consumer preference, and choice. Thus, food and agricultural industries seek rapid, simple, and nondestructive approaches to assess real-time measurements at the post-harvest stage before packaging for the consumer market. While sorting and grading may be performed by humans, it is unreliable, time-consuming, complicated, subjective, onerous, expensive, and easily influenced by surroundings. Therefore, an astute sorting and grading method for tomato fruit is required. We evaluated two tomato configurations on a conveyor belt: single tomatoes (no occlusion) and multi-tomatoes (partially occluded). We used polygon approximation for concave and convex point extraction algorithms to segment the occluded tomatoes. We developed seven models for regression using single-tomato image features. The Bayesian regularization artificial neural network outranked all the trained models in weight estimation with a root-mean-square error (RMSE) of 1.468 g and R2 of 0.971. For volume estimation, the RBF SVM had the best performance with R2 of 0.982 and RMSE of 1.2683 cm3. It is feasible to implement a proposed system as a noninvasive in-line sorting technique for tomatoes