Monitoring Photosynthesis by In Vivo Chlorophyll Fluorescence: Application to High-Throughput Plant Phenotyping

In spite of the decrease in the rate of population growth, world population is expected to rise from the current figure (slightly above to 7.2 billion) to reach 9.6 billion in 2050. There is therefore a pressing need to increase food production. Since most of the best arable lands are already under production, expanding the agricultural areas would have negative impacts on important natural areas. Thereby, increasing the productivity of the current agricultural areas is the chief objective of agronomical planners, and planting more productive and better adapted plant varieties is crucial to achieve it. In fact, plant breeding is at the forefront of concern of both agronomists and plant biologists. Plant breeding is a millenary activity that deeply changed our world. However, the use of molecular biology techniques jointly with informatics capabilities—giving rise to the omics techniques—deeply accelerated plant breeding, providing new and better plant varieties at an increased pace. The advances in genomics, though, far by-passed the advances in phenomics, and so there is a rising consensus among plant breeders that plant phenotyping is a bottleneck to advancing plant breeding. Therefore, a range of international initiatives in high-throughput plant phenotyping (HTPP) are at course, and new automated equipment is being developed. Phenotyping plants, however, is not a simple matter. To begin with, it has to be decided which parameters to measure in order to extrapolate to the desired goals, plant resistance and plant productivity. For this, as well as for plant breeding, an in-depth knowledge of plant physiology is required. Photosynthesis has been considered as a good indicator of overall plant performance. It is the only energy input in plants and thereby impacts all aspects of plant metabolism and physiology. The cumulative rate of photosynthesis over the growing season is the primary determinant of crop biomass. It largely determines the redox state of plant cells, and therefore, it is at the core of regulatory networks. Therefore, assessing photosynthesis and the photosynthetic apparatus plays a core role on plant phenotyping. Nevertheless, high-throughput phenotyping demands very rapid measurements, and consequently the most common method of photosynthesis measurement—the infra-red gas analysis—is not well suited for this purpose. On the contrary, the techniques based on in vivo chlorophyll (Chl) a fluorescence measurements are perfectly fit. In this chapter, an historical perspective on the development of in vivo Chl a measurement is briefly addressed. Then, the state of the art of the fluorescence-based techniques of photosynthesis assessment is presented, and their potential use in HTPP is evaluated. Finally, the current use of these techniques in the main systems of phenotyping is surveyed

Breeding to Optimize Agriculture in a Changing World

AbstractBreeding to Optimize Chinese Agriculture (OPTICHINA) was a three-year EU–China project launched in June of 2011. As designed, the project acted as a new strategic model to reinforce systematic cooperation on agricultural research between Europe and China. The OPTICHINA International Conference “Breeding to Optimize Agriculture in a Changing World” was held in Beijing, May 26–29, 2014. The conference included six thematic areas: (1) defining and protecting the yield potential of traits and genes; (2) high-throughput precision phenotyping in the field; (3) molecular technologies in modern breeding; (4) plant ideotype; (5) data analysis, data management, and bioinformatics; and (6) national challenges and opportunities for China. The 10 articles collected in this special issue represent key contributions and topics of this conference. This editorial provides a brief introduction to the OPTICHINA project, followed by the main scientific points of articles published in this special issue. Finally, outcomes from a brainstorming discussion at the end of the conference are summarized, representing the authors' opinions on trends in breeding for a changing world

Physiological and Biochemical Basis of Faba Bean Breeding for Drought Adaptation —A Review

Grain legumes are commonly used for food and feed all over the world and are the main source of protein for over a billion people worldwide, but their production is at risk from climate change. Water deficit and heat stress both significantly reduce the yield of grain legumes, and the faba bean is considered particularly susceptible. The genetic improvement of faba bean for drought adaptation (water deficit tolerance) by conventional methods and molecular breeding is time-consuming and laborious, since it depends mainly on selection and adaptation in multiple sites. The lack of high-throughput screening methodology and low heritability of advantageous traits under environmental stress challenge breeding progress. Alternatively, selection based on secondary characters in a controlled environment followed by field trials is successful in some crops, including faba beans. In general, measured features related to drought adaptation are shoot and root morphology, stomatal characteristics, osmotic adjustment and the efficiency of water use. Here, we focus on the current knowledge of biochemical and physiological markers for legume improvement that can be incorporated into faba bean breeding programs for drought adaptation.Peer reviewe

Влияние сорных растений и аммофоса на выход зерна гороха в степной зоне Южного Урала

Increasing the yield of pea grain in agricultural production is an important task for modern agriculture in the Orenburg region. To expand it, long-term studies on the influence of weeds and ammophos on the yield of pea grain were carried out. The article presents the research results for 2002-2019 by weediness of crops, the content of macronutrients and the productivity of pea grain in six-field and two-field crop rotations. Among the main factors affecting the yield of pea grain such as the total number of weeds and the content of accumulated nutrients in the 0-30 cm, soil layer were considered. The total number of weeds on two nutritional backgrounds in the pea germination phase was from 102.0 to 137.0. During the ripening period, respectively, it was from 44.0 to 56.0 pcs / m2. The maximum yield of pea grain is observed in crops after soft wheat with ammophos - 1.03 t / ha, without the use of fertilizer - 0.98 t / ha. The lowest yield of peas was obtained after durum wheat in a two-field crop rotation: according to the fertilized nutrition background - 0.76, unfertilized - 0.70 t / ha. As a result of statistical data processing in the third variant of the experiment (sowing peas after soft wheat in the aftereffect of a busy fallow), it was found that the increase in the productivity of pea grain slightly depended on the weediness of crops and the share of its influence ranged from 13.98 to 18.37%. However, the decrease in the yield of peas in the fifth variant of the experiment (sowing peas in alternation with durum wheat) was largely determined by weeds and the level of their influence was from 40.21 to 54.41%. The content of accumulated nitrate nitrogen and mobile phosphorus from ammophos before all sowing of peas in crop rotations ranged from 1.4 to 2.9 mg / 100 g of soil. The increase in pea grain from mineral fertilizers for 18 years, respectively, for all predecessors was 0.07; 0.05; 0.08 and 0.06 t / ha, except for the second variant of the experiment, where peas were sown after soft wheat in the aftereffect of black steam. The results of mathematical processing of the data on the increase in grain of peas show the effect of ammophos on the increase in grain by options in the range from 55.41 to 81.88%.Повышение урожайности зерна гороха в сельскохозяйственном производстве является важной задачей для современного земледелия Оренбуржья. Для ее решения проведены длительные исследования по влиянию сорных растений и аммофоса на выход зерна гороха. В статье приведены результаты исследований за 2002–2019 гг. по засорённости посевов, содержанию макроэлементов и продуктивности зерна гороха в шестипольных и двупольных севооборотах. Среди основных факторов, влияющих на выход зерна гороха, рассмотрены такие, как общее количество сорных растений и содержание накопленных питательных веществ в слое почвы 0–30 см. В среднем за период исследований наибольшая засорённость посевов наблюдается на делянках гороха после мягкой и твёрдой пшеницы. Общее количество сорных растений на двух фонах питания составило в фазе всходов гороха от 102,0 до 137,0, а в период созревания соответственно от 44,0 до 56,0 шт/м 2. Максимальный выход зерна гороха отмечается в посевах после мягкой пшеницы с аммофосом – 1,03 т/га, без применения удобрения – 0,98 т/га. Наименьшая урожайность гороха получена после твёрдой пшеницы в двупольном севообороте: по удобренному фону питания – 0,76, неудобренному – 0,70 т/га. В результате статистической обработки данных в третьем варианте эксперимента (посев гороха после мягкой пшеницы в последействии занятого пара) установлено, что повышение продуктивности зерна гороха незначительно зависело от засорённости посевов и доля ее влияния находилась в пределах от 13,98 до 18,37%. Однако снижение урожайности гороха в пятом варианте опыта (посев гороха в чередовании с твердой пшеницей) в значительной степени определялось сорными растениями и уровень их влияния составил от 40,21 до 54,41%. Содержание накопленного нитратного азота и подвижного фосфора от аммофоса перед всеми посевами гороха в севооборотах колебалось от 1,4 до 2,9 мг/100 г почвы. Прибавка зерна гороха от минерального удобрения за 18 лет соответственно по всем предшественникам составила 0,07; 0,05; 0,08 и 0,06 т/га, кроме второго варианта опыта, где горох высевали после мягкой пшеницы в последействии черного пара. Результаты математической обработки данных прибавки урожайности зерна гороха показывают влияние аммофоса на прибавку зерна по вариантам в диапазоне от 55,41 до 81,88 %

Pea breeding for resistance to rhizospheric pathogens

Pea (Pisum sativum L.) is a grain legume widely cultivated in temperate climates. It is important in the race for food security owing to its multipurpose low-input requirement and environmental promoting traits. Pea is key in nitrogen fixation, biodiversity preservation, and nutritional functions as food and feed. Unfortunately, like most crops, pea production is constrained by several pests and diseases, of which rhizosphere disease dwellers are the most critical due to their long-term persistence in the soil and difficulty to manage. Understanding the rhizosphere environment can improve host plant root microbial association to increase yield stability and facilitate improved crop performance through breeding. Thus, the use of various germplasm and genomic resources combined with scientific collaborative efforts has contributed to improving pea resistance/cultivation against rhizospheric diseases. This improvement has been achieved through robust phenotyping, genotyping, agronomic practices, and resistance breeding. Nonetheless, resistance to rhizospheric diseases is still limited, while biological and chemical-based control strategies are unrealistic and unfavourable to the environment, respectively. Hence, there is a need to consistently scout for host plant resistance to resolve these bottlenecks. Herein, in view of these challenges, we reflect on pea breeding for resistance to diseases caused by rhizospheric pathogens, including fusarium wilt, root rots, nematode complex, and parasitic broomrape. Here, we will attempt to appraise and harmonise historical and contemporary knowledge that contributes to pea resistance breeding for soilborne disease management and discuss the way forward

Unmanned aerial system and satellite-based high resolution imagery for high-throughput phenotyping in dry bean

Dry bean breeding programs are crucial to improve the productivity and resistance to biotic and abiotic stress. Phenotyping is a key process in breeding that refers to crop trait evaluation. In recent years, high-throughput plant phenotyping methods are being developed to increase the accuracy and efficiency for crop trait evaluations. In this study, aerial imagery at different resolutions were evaluated to phenotype crop performance and phenological traits using genotypes from two breeding panels, Durango Diversity Panel (DDP) and Andean Diversity Panel (ADP). The unmanned aerial system (UAS) based multispectral and thermal data were collected for two seasons at multiple time points (about 50, 60 and 75 days after planting/DAP in 2015; about 60 and 75 DAP in 2017). Four image-based features were extracted from multispectral images. Among different features, normalized difference vegetation index (NDVI) data were found to be consistently highly correlated with performance traits (above ground biomass, seed yield), especially during imaging at about 60–75 DAP (early pod development). Overall, correlations were higher using NDVI in ADP than DDP with biomass (r=−0.67 to −0.91 in ADP; r=−0.55 to −0.72 in DDP) and seed yield (r=0.51 to 0.73 in ADP; r=0.42 to 0.58 in DDP) at about 60 and 75 DAP. For thermal data, a temperature data normalization (utilizing common breeding plots in multiple thermal images) was implemented and the MEAN plot temperatures generally correlated significantly with biomass (r=0.28–0.88). Finally, lower resolution satellite images (0.05–5 m/pixel) using UAS data was simulated and image resolution beyond 50 cm was found to reduce the relationship between image features (NDVI) and performance variables (biomass, seed yield). Four different high resolution satellite images: Pleiades-1A (0.5 m), SPOT 6 (1.5 m), Planet Scope (3.0 m), and Rapid Eye (5.0 m) were acquired to validate the findings from the UAS data. The results indicated sub-meter resolution satellite multispectral imagery showed promising application in field phenotyping, especially when the genotypic responses to stress is prominent. The correlation between NDVI extracted from Pleiades-1A images with seed yield (r=0.52) and biomass (r=−0.55) were stronger in ADP; where the strength in relationship reduced with decreasing satellite image resolution. In future, we anticipate higher spatial and temporal resolution data achieved with low-orbiting satellites will increase applications for high-throughput crop phenotyping

Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewe