Coffee, one of the most heavily globally traded agricultural commodities, has been categorized as a highly sensitive plant species to progressive climatic change. Here, we summarize recent insights on the coffee plant’s physiological performance at elevated atmospheric carbon dioxide concentration [CO2]. We specifically (i) provide new data of crop yields obtained under free-air CO2 enrichment conditions, (ii) discuss predictions on the future of the coffee crop as based on rising temperature and (iii) emphasize the role of [CO2] as a key player for mitigating harmful effects of supra-optimal temperatures on coffee physiology and bean quality. We conclude that the effects of global warming on the climatic suitability of coffee may be lower than previously assumed. We highlight perspectives and priorities for further research to improve our understanding on how the coffee plant will respond to present and progressive climate change.

https://link.springer.com/content/pdf/10.1007%2Fs10584-018-2346-4.pdf

Why could the coffee crop endure climate change and global warming to a greater extent than previously estimated

Soybean is one of the most important oilseed and fodder crops. Benefiting from the efforts of soybean breeders and the development of breeding technology, large number of germplasm has been generated over the last 100 years. Nevertheless, soybean breeding needs to be accelerated to meet the needs of a growing world population, to promote sustainable agriculture and to address future environmental changes. The acceleration is highly reliant on the discoveries in gene functional studies. The release of the reference soybean genome in 2010 has significantly facilitated the advance in soybean functional genomics. Here, we review the research progress in soybean omics (genomics, transcriptomics, epigenomics and proteomics), germplasm development (germplasm resources and databases), gene discovery (genes that are responsible for important soybean traits including yield, flowering and maturity, seed quality, stress resistance, nodulation and domestication) and transformation technology during the past decade. At the end, we also briefly discuss current challenges and future directions.

Progress in soybean functional genomics over the past decade.

A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth

This paper is concerned with the digitization and visualization of potted greenhouse tomato plants in indoor environments. For the digitization, an inexpensive and efficient commercial stereo sensor—a Microsoft Kinect—is used to separate visual information about tomato plants from background. Based on the Kinect, a 4-step approach that can automatically detect and segment stems of tomato plants is proposed, including acquisition and preprocessing of image data, detection of stem segments, removing false detections and automatic segmentation of stem segments. Correctly segmented texture samples including stems and leaves are then stored in a texture database for further usage. Two types of tomato plants—the cherry tomato variety and the ordinary variety are studied in this paper. The stem detection accuracy (under a simulated greenhouse environment) for the cherry tomato variety is 98.4% at a true positive rate of 78.0%, whereas the detection accuracy for the ordinary variety is 94.5% at a true positive of 72.5%. In visualization, we combine L-system theory and digitized tomato organ texture data to build realistic 3D virtual tomato plant models that are capable of exhibiting various structures and poses in real time. In particular, we also simulate the growth process on virtual tomato plants by exerting controls on two L-systems via parameters concerning the age and the form of lateral branches. This research may provide useful visual cues for improving intelligent greenhouse control systems and meanwhile may facilitate research on artificial organisms.

Digitization and visualization of greenhouse tomato plants in indoor environments.

Structural and functional changes in coffee trees after 4 years under free air CO2 enrichment.

Water availability, light, management practices, and harvest time impacts on Coffea arabica L. yield and bean quality remain uncertain. It was hypothesized that the soil water and light availability could impact berry distribution, yield, and bean chemical attributes within the plant canopy. Therefore, it was aimed to study berry yield, berry distribution, and bean chemical traits along the canopy strata of four coffee genotypes (Iapar 59, Catuaí 99 and two Ethiopian wild accessions, ‘E083′ and ‘E027′), cultivated with (IRR) and without irrigation (NI) in the two initial harvest years. The maximum height of berry occurrence was lower in NI than in IRR plants in both harvest years. In the 2nd harvest year, higher leaf-to-fruit ratio was found under NI than under IRR for all genotypes, except for Catuaí 99, while the most regular berry distribution among canopy strata was obtained in IRR ‘E083′, the highest bean yield in IRR ‘E083′ and Iapar 59, and the highest percent of useful bean biomass in NI Catuaí 99. The reduced lipid content under IRR was more important in the 1st (all genotypes) than in the 2nd harvest year (Iapar 59 and ‘E027′). As a novelty, chemical bean composition was additionally impacted by light availability along the canopy strata. Proteins declined from bottom (shaded) to upper (highly light exposed) strata, regardless of genotype and harvest year. Similar stratification was observed in caffeine in the 2nd year. Although some traits were somewhat changed among strata, no substantial quality changes occurred, thus allowing that harvest might include the entire plant and not only some specific strata. Iapar 59 and ‘E083′ showed chemical composition usually associated with high bean quality, with the highest lipid, sucrose, and soluble sugar contents, and the lowest caffeine, chlorogenic acids, and phenolic components among four genotypes, but Iapar 59 plants were less affected in their yield under NI. Based on additional responses from space occupation and yield only under IRR, the wild accession ‘E083′ must be considered in future breeding programs as promising material for intensive input conditions. High bean quality and the less variated yield under lower soil water availability qualified the Iapar 59 as the most prominent among the four genotypes.

/pdf/variation-in-yield-berry-distribution-and-chemical-3qesy8g7.pdf

Variation in Yield, Berry Distribution and Chemical Attributes of Coffea arabica Beans among the Canopy Strata of Four Genotypes Cultivated under Contrasted Water Regimes

Cloudpde: a web solver of differential equations by finite difference discretization

Purpose – The aim of this paper is to propose a Web environment for pre-processing and post-processing for 2D problems in generalized coordinate systems. Design/methodology/approach – The system consists of a Web service for client-server communication, a database for user information, simulation requests and results storage, a module of (for) calculation processing (front-end) and a graphical interface for visualization of discretized mesh (back-end). Findings – The Web system was able to model real problems and situations, where the user can describe the problem or upload a geometry file descriptor, generated from computer graphics software. The Web system, programmed for finite difference solutions, was able to generate a mesh from other complex methods, such as finite elements method, adapting it to the proposed Web system, respecting the finite difference mesh structure. Research limitations/implications – The proposed Web system is limited to solve partial differential equations by finite difference...

Client-server architecture for pre and post-processing of real problems involving two-dimensional generalized coordinates

The artificial neural networks (ANN) are a solution to model the nonlinear systems. The monthly mean values of environmental and morphological data were used to build time series related to yerba-mate growth. Time series and data relative to rhythmic growth were used for ANN training. The final results of this FSPM are yerba-mate mock-ups, related to two particular environmental conditions.

/pdf/artificial-neural-networks-in-modeling-of-environmental-time-2yv0x7l9yx.pdf

Fabio Takeshi Matsunaga

Papers

Variation in Yield, Berry Distribution and Chemical Attributes of Coffea arabica Beans among the Canopy Strata of Four Genotypes Cultivated under Contrasted Water Regimes

Cloudpde: a web solver of differential equations by finite difference discretization

Client-server architecture for pre and post-processing of real problems involving two-dimensional generalized coordinates

Artificial neural networks in modeling of environmental time series for yerba-mate growth dynamics