Biological and Chemical Characterization of Musa paradisiaca Leachate.

There is a growing demand for molecules of natural origin for biocontrol and biostimulation, given the current trend away from synthetic chemical products. Leachates extracted from plantain stems were obtained after biodegradation of the plant material. To characterize the leachate, quantitative determinations of nitrogen, carbon, phosphorus, and cations (K+, Ca2+, Mg2+, Na+), Q2/4, Q2/6, and Q4/6 absorbance ratios, and metabolomic analysis were carried out. The potential role of plantain leachates as fungicide, elicitor of plant defense, and/or plant biostimulant was evaluated by agar well diffusion method, phenotypic, molecular, and imaging approaches. The plant extracts induced a slight inhibition of fungal growth of an aggressive strain of Colletotrichum gloeosporioides, which causes anthracnose. Organic compounds such as cinnamic, ellagic, quinic, and fulvic acids and indole alkaloid such as ellipticine, along with some minerals such as potassium, calcium, and phosphorus, may be responsible for the inhibition of fungal growth. In addition, jasmonic, benzoic, and salicylic acids, which are known to play a role in plant defense and as biostimulants in tomato, were detected in leachate extract. Indeed, foliar application of banana leachate induced overexpression of LOXD, PPOD, and Worky70-80 genes, which are involved in phenylpropanoid metabolism, jasmonic acid biosynthesis, and salicylic acid metabolism, respectively. Leachate also activated root growth in tomato seedlings. However, the main impact of the leachate was observed on mature plants, where it caused a reduction in leaf area and fresh weight, the remodeling of stem cell wall glycopolymers, and an increase in the expression of proline dehydrogenase.

Starch granule size and shape characterization of yam (Dioscorea alata L.) flour using automated image analysis.

BACKGROUND: Roots, tubers and bananas (RTB) play an essential role as staple foods, particularly in Africa. Consumer acceptance for RTB products relies strongly on the functional properties of, which may be affected by the size and shape of its granules. Classically, these are characterized either using manual measurements on microscopic photographs of starch colored with iodine, or using a laser light-scattering granulometer (LLSG). While the former is tedious and only allows the analysis of a small number of granules, the latter only provides limited information on the shape of the starch granule. RESULTS: In this study, an open-source solution was developed allowing the automated measurement of the characteristic parameters of the size and shape of yam starch granules by applying thresholding and object identification on microscopic photographs. A random forest (RF) model was used to predict the starch granule shape class. This analysis pipeline was successfully applied to a yam diversity panel of 47 genotypes, leading to the characterization of more than 205 000 starch granules. Comparison between the classical and automated method shows a very strong correlation (R2 = 0.99) and an absence of bias for granule size. The RF model predicted shape class with an accuracy of 83%. With heritability equal to 0.85, the median projected area of the granules varied from 381 to 1115 µm2 and their observed shapes were ellipsoidal, polyhedral, round and triangular. CONCLUSION: The results obtained in this study show that the proposed open-source pipeline offers an accurate, robust and discriminating solution for medium-throughput phenotyping of yam starch granule size distribution and shape classification. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

BACKGROUND: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. RESULTS: This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2 ), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). CONCLUSION: According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Quantitative trait loci and candidate genes for physico-chemical traits related to tuber quality in greater yam (Dioscorea alata L.).

BACKGROUND: Starch, dry matter content (DMC), proteins, and sugars are among the major influences on yam tuber quality. Genetic improvement programs need simple, rapid, and low-cost tools to screen large populations. The objectives of this work were, using a quantitative trait loci mapping approach (QTL) on two diploid full-sib segregating populations, (i) to acquire knowledge about the genetic control of these traits; (ii) to identify markers linked to the genomic regions controlling each trait, which are useful for marker-assisted selection (MAS); (iii) to validate the QTLs on a diversity panel; and (iv) to identify candidate genes from the validated QTLs. RESULTS: Heritability for all traits was moderately high to high. Significant correlations were observed between traits. A total of 25 QTLs were identified, including six for DMC, six for sugars, six for proteins, and seven for starch. The phenotypic variance explained by individual QTLs ranged from 14.3% to 28.6%. The majority of QTLs were validated on a diversity panel, showing that they are not specific to the genetic background of the progenitors. The approximate physical location of validated QTLs allowed the identification of candidate genes for all studied traits. Those detected for starch content were mainly enzymes involved in starch and sucrose metabolism, whereas those detected for sugars were mainly involved in respiration and glycolysis. CONCLUSION: The validated QTLs will be useful for breeding programs using MAS to improve the quality of yam tubers. The putative genes should be useful in providing a better understanding of the physiological and molecular basis of these important tuber quality traits. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Near-infrared spectroscopy applications for high-throughput phenotyping for cassava and yam: A review.

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.

Acromyrmex octospinosus (Hymenoptera: Formicidae) management: effects of TRAMILs fungicidal plant extracts.

Leaf-cutting ants, Acromyrmex octospinosus (Reich), are considering among the most important pest species of the New World. Until now, the main insecticides used for controlling these ants were synthetic chemicals. Leaf-cutting ants live in obligate symbiosis with abasidiomycete fungus, Leucocoprinus gongylophorus (Heim) Moeller. The crucial role of this symbiotic partner in the nest of leaf-cutting ants has prompted us to focus on A. octospinosus management through the use of fungicides in our study. Five parts of plants identified for their antifungal potential through TRAMIL ethnopharmacological surveys were tested: 1) bulbs of Allium cepa L.; 2) seed pods of Allium sativum L.; 3) green fruits of Lycopersicon esculentum L.; 4) leaves of Manihot esculenta Crantz; and 5) leaves of Senna alata (L.) Roxburgh. One plant extract with strong fungicidal activity (S. alata) against L. gongylophorus was found. The other extracts had lesser fungistatic or fungicidal effects depending on the concentrations used. The data presented in this study showed that TRAMILs fungicidal plant extracts have potential to control the symbiotic fungus of leaf cutting ants, in particular a foliage extract of S. alata.

Effects of nitrogen source and defoliation on growth and biological dinitrogen fixation of Gliricidia sepium seedlings.

Effects of four N sources and two defoliation treatments on growth and nitrogenase activity of Gliricidia sepium (Jacq.) Walp seedlings were studied in a greenhouse. All nutrients were supplied in irrigation water to the sterile growing medium. The N sources were: (1) 100 mg l(-1) of N supplied as NO(3) (-) (high-NO(3) (-)), (2) 50 mg l(-1) of N supplied as NO(3) (-) and inoculation with Rhizobium spp. medium-NO(3) (-)), (3)100 mg l(-1) of N supplied as NH(4)NO(3), and (4) inoculation with Rhizobium spp without mineral N (N(2)). At 35 weeks after sowing, mean total biomass was 130.5, 50.5, 22.9 and 17.4 g seedling(-1) in the NH(4)NO(3), N(2), medium-NO(3) (-) and high-NO(3) (-) treatments, respectively. The root/shoot ratio was high in all of the N treatments (1.73-2.77) because the seedlings had big taproots. The medium-NO(3) (-) treatment completely inhibited nodulation, whereas seedlings in the N(2) treatment were profusely nodulated. At 32 weeks after sowing, groups of seedlings in the N(2) and high-NO(3) (-) treatments were subjected to 50 or 100% defoliation. Closed-chamber acetylene reduction assays of intact root systems were conducted to compare nitrogenase activity at 7, 14 and 28 days after defoliation (DAD). At 7 and 14 DAD, nitrogenase activity of completely and partially defoliated seedlings was about 10 and 60%, respectively, of that of undefoliated controls. At 28 DAD, nitrogenase activity of completely defoliated seedlings was twice the predefoliation value, whereas nitrogenase activity of partially defoliated seedlings was only 87% of the predefoliation value. Recovery of nitrogenase activity was strongly correlated with foliage regrowth in the completely defoliated seedlings, but not in the partially defoliated seedlings. Abundant belowground C and N reserves in the large taproot probably contributed to the rapid recovery from defoliation. Accumulation of belowground biomass may also improve defoliation tolerance of mature trees.