Digital descriptors sharpen classical descriptors, for improving genebank accession management: A case study on Arachis spp. and Phaseolus spp.

High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. Our work proposes to improve the characterization processes of bean and peanut accessions in the CIAT genebank through the identification of phenomic descriptors comparable to classical descriptors including methodology integration into the genebank workflow. To cope with these goals morphometrics and colorimetry traits of 14 bean and 16 forage peanut accessions were determined and compared to the classical International Board for Plant Genetic Resources (IBPGR) descriptors. Descriptors discriminating most accessions were identified using a random forest algorithm. The most-valuable classification descriptors for peanuts were 100-seed weight and days to flowering, and for beans, days to flowering and primary seed color. The combination of phenomic and classical descriptors increased the accuracy of the classification of Phaseolus and Arachis accessions. Functional diversity indices are recommended to genebank curators to evaluate phenotypic variability to identify accessions with unique traits or identify accessions that represent the greatest phenotypic variation of the species (functional agrobiodiversity collections). The artificial intelligence algorithms are capable of characterizing accessions which reduces costs generated by additional phenotyping. Even though deep analysis of data requires new skills, associating genetic, morphological and ecogeographic diversity is giving us an opportunity to establish unique functional agrobiodiversity collections with new potential traits.

Phenological variations of avocado cv. Hass and their relationship with thermal time under tropical conditions.

In recent years, the avocado has been one of the most dynamic fruits in the world market. In particular, cv. Hass stands out due to its productivity, nutritional quality, and acceptance. Under tropical conditions, weather elements, especially air temperature, and precipitation, affect the productivity and quality of cv. Hass. However, in tropical environments, many relationships between weather and phenological aspects of this cultivar are still unknown. Given this situation, our aim was to identify the variation and degree of association between the phenology of avocado cv. Hass and thermal time (TT) under low-latitude conditions. Eight commercial fields planted with cv. Hass grafted onto Antillean genotypes, located in an altitudinal transect between 1,700 and 2,500 m, were evaluated. The evaluation was carried out for three years and was focused on determining the differences in avocado phenological patterns associated with different environmental variables monitored by weather stations at each location. Air temperature data were used to calculate the base temperature (BT) using different methods for all phenological stages. Later the TT was determined for each stage and all locations. The results show that the duration of each phenological stage varies as a function of elevation (air temperature) and that the phenological stages overlap at the regional, crop field, and plant levels at different periods of the year, generating a high phenological variability but with specific patterns associated with temperature and precipitation. The BT for each phenological stage varied between 0.3 and 7.5 °C, and TT was found to vary depending on the method of calculation. Our work suggests that the generalization of a BT of 10 °C is not applicable for avocado cv. Hass crops under tropical conditions, specifically in the case of Colombia. Detailed studies of phenological relationships with respect to climatic variables will allow a better approximation of the productive behavior of avocado cv. Hass.

Calcium phosphate nanoparticles improve growth parameters and mitigate stress associated with climatic variability in avocado fruit.

The avocado cv. Hass is one of the most dynamic fruits in the world and is of particular significance in tropical areas, where climate variability phenomena have a high impact on productivity and sustainability. Nanotechnology-based tools could be an alternative to mitigate and/or adapt plants to these phenomena. Our approach was based on identifying changes in temperature and precipitation associated with climate variability in avocado areas in Colombia and proposing mitigation strategies based on the use of nanotechnology. This study had two objectives: (i) to identify variations in temperature and precipitation in avocado-producing areas in Colombia and (ii) to evaluate the effect of calcium phosphate nanoparticles (nano CP) as an alternative to reduce stress in avocados under simulate climatic variability condition. Climatic clusters were determined based on the spatial K-means method and with the climatic temporal series data (1981-2020), a time series analysis we carried out. Later changes in each cluster were simulated in growth chambers, evaluating physiological and developmental responses in avocado seedlings subjected to nanoCaP after adjusting the application form and dose. XRD diffraction shows that the calcium phosphate phases obtained by solution combustion correspond to a mixture of hydroxyapatite and witocklite nanoparticles with irregular morphologies and particle sizes of 100 nm. Three clusters explained ∼90% of the climate variation, with increases and decreases in temperature and precipitation in the range of 1-1.4 °C and 4.1-7.3% respectively. The best-fitted time series models were of stationary autoregressive integrated moving averages (SARIMA). The avocado seedlings had differential responses (P<0.05) depending on the clusters, with a decrease in physiological behavior and development between 10 and 35%. Additionally, the nanoCaP reduced the climatic stress (P< 0.05) in a range between 10 and 22.5%. This study identified the negative effect of climate variability on avocado seedlings and how nanoCaP can mitigate these phenomena.

Close-Range Thermography and Reflectance Spectroscopy Support In Vitro and In Vivo Characterization of Colletotrichum spp. Isolates from Mango Fruits.

Colletotrichum causing anthracnose in mango is known for its variable virulence that may have an effect on disease development and efficacy of management strategies. In this study, we characterized Colletotrichum spp. isolated from mango fruits under in vitro and in vivo conditions using close-range thermography and reflectance spectroscopy. Twenty-six isolates were phylogenetically characterized to ascertain species using the internal transcribed spacer sequence. Virulence, spectral (in vivo and in vitro), and thermographic responses (in vivo) of these isolates were analyzed. Isolates were grouped into the Colletotrichum gloeosporioides species complex and classified into eight morphotypes. Mycelial growth, conidia production, sporulation abundance, and area under disease progress curve (AUDPC) varied largely among isolates. Disease symptoms were observed 4 days after inoculation (dai), and, for most morphotypes, changes in tissue temperature were registered at 11 dai, with the greatest decrease at 14 dai with pathogen sporulation. In vitro and in vivo morphotypes shared changes in the spectrum range, and main variations were found in the number of informative spectral bands. In vivo average gross reflectance was higher in disease-inoculated tissue than in healthy uninoculated tissue. Morphotype responses varied depending on AUDPC values and postinoculation time. Discriminant analysis of the spectral response using principal component analysis and partial least squares regression explained 94 to 96.3 and 98 to 99.9% of the variance from in vitro and in vivo tests, respectively. Spectral markers were obtained for four distinct morphotype groups. We found three (550 to 650, 650.1 to 790, and 1,300 to 1,400 nm) and two (520 to 830 and 1,100 to 1,450 nm) regions with highly (P < 0.05) discriminant spectral bands for diseased fruits and morphotype characterization.

Physiological Response of Cape Gooseberry Plants to Fusarium oxysporum f. sp. physali, Fusaric Acid, and Water Deficit in a Hydrophonic System.

Cape gooseberry production has been limited by vascular wilt caused by Fusarium oxysporum f. sp. physali (Foph). Fusaric acid (FA) is a mycotoxin produced by many Fusarium species such as F. oxysporum formae speciales. The effects of the interaction between this mycotoxin and plants (such as cape gooseberry) under biotic stress (water deficit, WD) have been little explored. Three experiments were carried out. The objectives of this study were to evaluate (i) different Foph inoculum densities (1 × 104 and 1 × 106 conidia ml-1; experiment (1); (ii) the effect of times of exposure (0, 6, 9, and 12 h) and FA concentrations (0, 12.5, 25, 50, and 100 mg L-1; experiment (2), and (iii) the interaction between Foph (1 × 104 conidia mL-1) or FA (25 mg L-1 × 9 h), and WD conditions (experiment 3) on the physiological (plant growth, leaf stomatal conductance (g s ), and photochemical efficiency of PSII (Fv/Fm ratio) and biochemical [malondialdehyde (MDA) and proline] responses of cape gooseberry seedling ecotype Colombia. The first experiment showed that Foph inoculum density of 1 × 106 conidia ml-1 caused the highest incidence of the disease (100%). In the second experiment, g s (~40.6 mmol m-2 s-1) and Fv/Fm ratio (~0.59) decreased, whereas MDA (~9.8 µmol g-1 FW) increased in plants with exposure times of 9 and 12 h and an FA concentration of 100 mg L-1 compared with plants without FA exposure or concentrations (169.8 mmol m-2 s-1, 0.8, and 7.2 µmol g-1 FW for g s , Fv/Fm ratio and MDA, respectively). In the last experiment, the interaction between Foph or FA and WD promoted a higher area under the disease progress curve (AUDPC) (Foph × WD = 44.5 and FA × WD = 37) and lower g s (Foph × WD = 6.2 mmol m-2 s-1 and FA × WD = 9.5 mmol m-2 s-1) compared with plants without any interaction. This research could be considered as a new approach for the rapid scanning of responses to the effects of FA, Foph, and WD stress not only on cape gooseberry plants but also on other species from the Solanaceae family.

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks.

Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization.

Postharvest diseases and disorders in avocado cv. Hass and their relationship to preharvest management practices.

Postharvest diseases and disorders are two of the most important parameters associated with the quality of avocado fruit. The aim of this study was to identify postharvest diseases and disorders in Hass avocado plots and to evaluate their relationships with different preharvest agronomical practices. This work was developed in 20 commercial plots of Hass avocado dedicated to production for national and export markets. The first part of this work was associated with the identification and characterization of diseases and disorders related with postharvest of avocado. In addition, it was determined the incidence of each disease and disorder based on simulation of postharvest scenarios for the national and export markets. Using a multinomial logistic regression model, it was possible to determine that the presence of each disease and disorder were related to crop management practices, soil and leaf and fruit nutrients levels. Most relevant postharvest disease and disorders were anthracnose, stem-end rot, chilling injury, and lenticel damage. Additionally, variables such as dry matter, plant pruning, and tissue concentration of Ca+2 were related with some pathologies and disorders. This work presents an advance in the recognition of postharvest diseases and disorders in avocado under tropical conditions, besides determining the main edaphic and anthropogenic associated factors.

Mitigation of the Adverse Effects of the El Niño (El Niño, La Niña) Southern Oscillation (ENSO) Phenomenon and the Most Important Diseases in Avocado cv. Hass Crops.

Areas cultivated with Hass avocado crops in Colombia have growth rapidly. One of the major limitations is the avocado wilt complex disease (AWC) caused by biotic and abiotic factors which have increased under the El Niño southern oscillation ENSO phenomenon (El Niño, La Niña). The objective of this study was to evaluate different strategies for mitigating the adverse effects associated with the ENSO phenomenon and AWC in avocado crops. We evaluated native materials, mulches, and parameters associated with the production of seedlings and planting practices in the field. The response variables tested were plant development, incidence, severity, mortality, and microbial dynamics, among others. The results indicated that native genotypes of Persea americana had different levels of adaptability to drought and flooding conditions. These genotypes also showed some degree of resistance to Phytophthora cinnamomi and Verticillium sp. infection with several degrees of rootstock-scion incompatibility with the Hass cultivar. In addition, mulch reduced the variability of soil moisture and temperature in the soil profile. Adequate selection of genotypes and new tools for planting have decreased the susceptibility to adverse effects associated with the ENSO phenomenon and the incidence and mortality caused by diseases under drought and flooding conditions. This work presents alternatives to mitigate adverse effects of climate variability in avocado crops under tropical conditions.