Increasing Air Temperatures and Its Effects on Growth and Productivity of Tomato in South Florida

Genetic and functional traits limit the success of colonisation by arbuscular mycorrhizal fungi in a tomato wild relative

To understand whether domestication had an impact on susceptibility and responsiveness to arbuscular mycorrhizal fungi (AMF) in tomato (Solanum lycopersicum), we investigated two tomato cultivars ("M82" and "Moneymaker") and a panel of wild relatives including S. neorickii, S. habrochaites and S. pennellii encompassing the whole Lycopersicon clade. Most genotypes revealed good AM colonisation levels when inoculated with the AMF Funneliformis mosseae. By contrast, both S. pennellii accessions analysed showed a very low colonisation, but with normal arbuscule morphology, and a negative response in terms of root and shoot biomass. This behaviour was independent of fungal identity and environmental conditions. Genomic and transcriptomic analyses revealed in S. pennellii the lack of genes identified within QTLs for AM colonisation, a limited transcriptional reprogramming upon mycorrhization and a differential regulation of strigolactones and AM-related genes compared to tomato. Donor plants experiments indicated that the AMF could represent a cost for S. pennellii: F. mosseae could extensively colonise the root only when it was part of a mycorrhizal network, but a higher mycorrhization led to a higher inhibition of plant growth. These results suggest that genetics and functional traits of S. pennellii are responsible for the limited extent of AMF colonisation.

A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data

The tomato as a raw material for processing is globally important and is pivotal in dietary and agronomic research due to its nutritional, economic, and health significance. This study explored the potential of machine learning (ML) for predicting tomato quality, utilizing data from 48 cultivars and 28 locations in Hungary over 5 seasons. It focused on °Brix, lycopene content, and colour (a/b ratio) using extreme gradient boosting (XGBoost) and artificial neural network (ANN) models. The results revealed that XGBoost consistently outperformed ANN, achieving high accuracy in predicting °Brix (R² = 0.98, RMSE = 0.07) and lycopene content (R² = 0.87, RMSE = 0.61), and excelling in colour prediction (a/b ratio) with a R² of 0.93 and RMSE of 0.03. ANN lagged behind particularly in colour prediction, showing a negative R² value of -0.35. Shapley additive explanation's (SHAP) summary plot analysis indicated that both models are effective in predicting °Brix and lycopene content in tomatoes, highlighting different aspects of the data. SHAP analysis highlighted the models' efficiency (especially in °Brix and lycopene predictions) and underscored the significant influence of cultivar choice and environmental factors like climate and soil. These findings emphasize the importance of selecting and fine-tuning the appropriate ML model for enhancing precision agriculture, underlining XGBoost's superiority in handling complex agronomic data for quality assessment.

Finding optimum climatic parameters for high tomato yield in Benin (West Africa) using frequent pattern growth algorithm

Tomato is one of the most appreciated vegetables in the world. Predicting its yield and optimizing its culture is important for global food security. This paper addresses the challenge of finding optimum climatic values for a high tomato yield. The Frequent Pattern Growth (FPG) algorithm was considered to establish the associations between six climate variables: minimum and maximum temperatures, maximum humidity, sunshine (Sun), rainfall, and evapotranspiration (ET), collected over 26 years in the three agro-ecological Zones of Benin. Monthly climate data were aggregated with yield data over the same period. After aggregation, the data were transformed into 'low', 'medium', and 'high' attributes using the threshold values defined. Then, the rules were generated using the minimum support set to 0.2 and the confidence to 0.8. Only the rules with the consequence 'high yield' were screened. The best yield patterns were observed in the Guinean Zone, followed by the Sudanian. The results indicated that high tomato yield was associated with low ET in all areas considered. Minimum and maximum temperatures, maximum humidity, and Sun were medium in every Zone. Moreover, rainfall was high in the Sudanian Zone, unlike the other regions where it remained medium. These results are useful in assessing climate variability's impact on tomato production. Thus, they can help farmers make informed decisions on cultivation practices to optimize production in a changing environment. In addition, the findings of this study can be considered in other regions and adapted to other crops.

Evaluation of Irrigation Modes for Greenhouse Drip Irrigation Tomatoes Based on AquaCrop and DSSAT Models

The improvement of the simulation accuracy of crop models in different greenhouse environments would be better applied to the automation management of greenhouse cultivation. Tomatoes under drip irrigation in a greenhouse were taken as the research object, and the cumulative evaporation capacity (Ep) of the 20 cm standard evaporation dish was taken as the basis for irrigation. Three treatments were set up in the experiment: high water treatment without mulch (NM-0.9 Ep), high water treatment with mulch (M-0.9 Ep), and low water treatment with mulch (M-0.5 Ep). AquaCrop and DSSAT models were used to simulate the canopy coverage, soil water content, biomass, and yield of the tomatoes. Data from 2020 were used to correct the model, and simulation results from 2021 were analyzed in this paper. The results showed that: (1) Of the two crop models, the simulation accuracy of the greenhouse tomato canopy coverage kCC was higher, and the root mean square errors were less than 6.8% (AquaCrop model) and 8.5% (DSSAT model); (2) The AquaCrop model could accurately simulate soil water change under high water treatments, while the DSSAT model was more suitable for the conditions without mulch; (3) The relative error RE of simulated and observed values for biomass B, yield Y, and water use efficiency WUE in the AquaCrop model were less than 2.0%, 2.3%, and 9.0%, respectively, while those of the DSSAT model were less than 4.7%, 7.6%, and 10.4%, respectively; (4) Considering the simulation results of each index comprehensively, the AquaCrop model was superior to the DSSAT model; subsequently, the former was used to predict 16 different water and film coating treatments (S1-S16). It was found that the greenhouse tomato yield and WUE were the highest under S7 (0.8 Ep), at 8.201 t/ha and 2.79 kg/m3, respectively.

Discovering Tolerance—A Computational Approach to Assess Abiotic Stress Tolerance in Tomato Under Greenhouse Conditions

Modern plant cultivars often possess superior growth characteristics, but within a limited range of environmental conditions. Due to climate change, crops will be exposed to distressing abiotic conditions more often in the future, out of which heat stress is used as example for this study. To support identification of tolerant germplasm and advance screening techniques by a novel multivariate evaluation method, a diversity panel of 14 tomato genotypes, comprising Mediterranean landraces of Solanum lycopersicum, the cultivar “Moneymaker” and Solanum pennellii LA0716, which served as internal references, was assessed toward their tolerance against long-term heat stress. After 5 weeks of growth, young tomato plants were exposed to either control (22/18°C) or heat stress (35/25°C) conditions for 2 weeks. Within this period, water consumption, leaf angles and leaf color were determined. Additionally, gas exchange and leaf temperature were investigated. Finally, biomass traits were recorded. The resulting multivariate dataset on phenotypic plasticity was evaluated to test the hypothesis, that more tolerant genotypes have less affected phenotypes upon stress adaptation. For this, a cluster-analysis-based approach was developed that involved a principal component analysis (PCA), dimension reduction and determination of Euclidean distances. These distances served as measure for the phenotypic plasticity upon heat stress. Statistical evaluation allowed the identification and classification of homogeneous groups consisting each of four putative more or less heat stress tolerant genotypes. The resulting classification of the internal references as “tolerant” highlights the applicability of our proposed tolerance assessment model. PCA factor analysis on principal components 1–3 which covered 76.7% of variance within the phenotypic data, suggested that some laborious measure such as the gas exchange might be replaced with the determination of leaf temperature in larger heat stress screenings. Hence, the overall advantage of the presented method is rooted in its suitability of both, planning and executing screenings for abiotic stress tolerance using multivariate phenotypic data to overcome the challenge of identifying abiotic stress tolerant plants from existing germplasms and promote sustainable agriculture for the future.

Psychrophilic Bacterial Phosphate-Biofertilizers: A Novel Extremophile for Sustainable Crop Production under Cold Environment

Abiotic stresses, including low-temperature environments, adversely affect the structure, composition, and physiological activities of soil microbiomes. Also, low temperatures disturb physiological and metabolic processes, leading to major crop losses worldwide. Extreme cold temperature habitats are, however, an interesting source of psychrophilic and psychrotolerant phosphate solubilizing bacteria (PSB) that can ameliorate the low-temperature conditions while maintaining their physiological activities. The production of antifreeze proteins and expression of stress-induced genes at low temperatures favors the survival of such organisms during cold stress. The ability to facilitate plant growth by supplying a major plant nutrient, phosphorus, in P-deficient soil is one of the novel functional properties of cold-tolerant PSB. By contrast, plants growing under stress conditions require cold-tolerant rhizosphere bacteria to enhance their performance. To this end, the use of psychrophilic PSB formulations has been found effective in yield optimization under temperature-stressed conditions. Most of the research has been done on microbial P biofertilizers impacting plant growth under normal cultivation practices but little attention has been paid to the plant growth-promoting activities of cold-tolerant PSB on crops growing in low-temperature environments. This scientific gap formed the basis of the present manuscript and explains the rationale for the introduction of cold-tolerant PSB in competitive agronomic practices, including the mechanism of solubilization/mineralization, release of biosensor active biomolecules, molecular engineering of PSB for increasing both P solubilizing/mineralizing efficiency, and host range. The impact of extreme cold on the physiological activities of plants and how plants overcome such stresses is discussed briefly. It is time to enlarge the prospects of psychrophilic/psychrotolerant phosphate biofertilizers and take advantage of their precious, fundamental, and economical but enormous plant growth augmenting potential to ameliorate stress and facilitate crop production to satisfy the food demands of frighteningly growing human populations. The production and application of cold-tolerant P-biofertilizers will recuperate sustainable agriculture in cold adaptive agrosystems.

Technology, Policy, and Market Adaptation Mechanisms for Sustainable Fresh Produce Industry: The Case of Tomato Production in Florida, USA

Tomato (Solanum lycopersicum L.) is an important vegetable crop in Florida, a state located in the south-eastern region of the United States. The state is the second largest producer of tomatoes in the country and contributes to almost 90% of the domestic winter tomato supplies. However, tomato farmers in Florida have come under increasing pressure due to climate changes, foreign imports, and rising production costs. The purpose of this paper is to analyze whether Florida tomato growers will continue to sustain their production given the seasonal and geographic production advantage, yet against various internal and external threats emerging throughout the fresh produce supply chain. We developed our study on a multi-disciplinary conceptual model of network (supply chain) relationship and primary and secondary data gathered from various stakeholders and the literature. We found that Florida farmers have done remarkably well by adapting to warming temperatures and changing consumer expectations about environmental sustainability and responsible labor practices. However, foreign competition, labor shortage, the rising costs of inputs, extreme weather events (hurricanes), and pests and diseases due to humid climate continue to affect the sustainability of the Florida tomato production. Our paper suggests various farm-, market-, and institution-level adaptation mechanisms for preventing the regional production advantage of the Florida tomato industry from eroding. Newer immigration laws are necessary for easing the labor situation. In order to have a level playing field with respect to the use of protected agriculture technology such as in Mexico and Canada, U.S. farmers in general and Florida farmers in particular need government support. Florida farmers need to diversify their fresh produce market strategies, finding new product streams. There is also a need for reforming the product certification landscape, which some growers find cumbersome and cost prohibitive. Growers may gain from being better able to convey to consumers the information regarding their effort put into environmental sustainability, workers welfare, and safe food.