Gliricidia sepium (Jacq.) Walp is a well-known agroforestry leguminous tree that provides multiple benefits in different agroecological zones. Its apparent versatility is seen in improving animal feed, cleaning environmental wastes, and healing inflammations. It was also found to have significant benefits in agroforestry due to its ability to enhance soil fertility through nitrogen fixation and green manure. However, this article reviews the use of Gliricidia sepium to improve soil fertility and crop agronomic and nutritional properties. Google Scholar, PubMed, and Science Direct were the databases consulted for the relevant articles used in this review. Trees and leaves of G. sepium, either used as mulch, biochar, or intercropped, have enhanced soil fertility indicators, such as total soil carbon, nitrogen, phosphorus, available phosphorus, pH, cation exchange capacity, and soil organic matter in different farming systems. Its immense positive performance in improving the yield of crops led to an economic advantage for low-income farmers. G. sepium can also lower the use of mineral fertilizer as its adoption grows, leading to a greener environment in the agricultural sector. The review concluded that there is a plethora of research on the effect of Gliricidia on maize yield enhancement; hence further investigations should be conducted on using Gliricidia sepium as a green fertilizer to improve yields and the nutritional properties of other crops.

Within communities in Osun and Imo States of Nigeria, farmer–processors grew and processed a diverse set of improved and landrace cassava varieties into the locally popular foods, gari, eba and fufu. Local and 15 main varieties were grown in a ‘mother and baby trials’ design in each state. Mother trials with three replications were processed by farmer–processors renown in their community for their processing skills. Baby trials were managed and processed by other farmer–processors. The objective was to identify food quality criteria to inform demand‐led breeding to benefit users, especially women, given their key roles in processing. Farmer–processors evaluated the overall quality of fresh roots and derived food products through pairwise comparisons. Improved varieties had higher fresh and dry root yield. Overall, landraces ranked first for quality of gari and eba, but several improved varieties were also appreciated for good quality. Landraces in Osun had higher gari yield and a higher swelling power compared to improved varieties. Colour (browning), bulk density, swelling power, solubility and water absorption capacity were the criteria most related to food product ranking by farmer–processors. Evaluation of varieties under farmer–processors’ conditions is crucial for providing guidance to breeders on critical selection criteria.

Hyperspectral imaging (HSI) is one of the most often used techniques for rapid quality evaluation for various applications. It is a non-destructive technique that effectively evaluates the quality attributes of root and tuber crops, including yam and cassava, and their food products. Hyperspectral imaging technology, which combines spectroscopy and imaging principles, has an advantage over conventional spectroscopy due to its ability to simultaneously evaluate the physical characteristics and chemical components of various food products and specify their spatial distributions. HSI has demonstrated significant potential for obtaining quick information regarding the chemical composition of the root and tuber, such as starch, protein, dry matter, amylose, and soluble sugars, as well as physical characteristics such as textural properties and water binding capacity. This review highlights the principles of near-infrared hyperspectral imaging (NIR-HSI) techniques combined with relevant image processing tools. It then provides cases of its application in determining crucial biochemical quality traits and textural attributes of roots and tuber crops, focusing on cassava and yam. The need for more information on using NIR-HSI in the quality evaluation of yam and cassava was underscored. It also presents the challenges and prospects of this technology.

Background. Unsound recycling of e-waste releases toxic metals into environmental media and has deleterious health consequences to humans as the metals transfer to humans through the food chain, direct contact and inhalation. Objectives. This study assessed soil contamination with lead (Pb), copper (Cu), chromium (Cr), nickel (Ni) and cadmium (Cd) arising from crude e-waste recycling. Methods. Forty-eight soil samples were collected from the vicinity of high-, medium- and low-activity recycling operations in Ogunpa in Ibadan, Nigeria as well as from the botanical garden of the University of Ibadan for background samples. Total extractable metals were leached with aqua regia and the leachates were analyzed using flame atomic absorption spectrometry. Speciation analysis was also conducted on soil samples that showed high concentrations of metals to determine the distributions in various phases. Results. All soil samples were determined to be sandy loam in composition with pH and organic matter ranging from ...

Summary: The review aimed to identify the different high‐throughput phenotyping (HTP) techniques that used for quality evaluation in cassava and yam breeding programmes, and this has provided insights towards the development of metrics and their application in cassava and yam improvements. A systematic review of the published research articles involved the use of NIRS in analysing the quality traits of cassava and yam was carried out, and Scopus, Science Direct, Web of Sciences and Google Scholar were searched. The results of the review established that NIRS could be used in understanding the chemical constituents (carbohydrate, protein, vitamins, minerals, carotenoids, moisture, starch, etc.) for high‐throughput phenotyping. This study provides preliminary evidence of the application of NIRS as an efficient and affordable procedure for HTP. However, the feasibility of using mid‐infrared spectroscopy (MIRS) and hyperspectral imaging (HSI) in combination with the NIRS could be further studied for quality traits phenotyping.

The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec^® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R²_P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H² > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.