Importance of the description of light interception in crop growth models

Rice plants treated with biochar derived from Spirulina (Arthrospira platensis) optimize resource allocation towards seed production

The use of biofertilizers is becoming an economical and environmentally friendly alternative to promote sustainable agriculture. Biochar from microalgae/cyanobacteria can be applied to enhance the productivity of food crops through soil improvement, slow nutrient absorption and release, increased water uptake, and long-term mitigation of greenhouse gas sequestration. Therefore, the aim of this study was to evaluate the stimulatory effects of biochar produced from Spirulina (Arthrospira platensis) biomass on the development and seed production of rice plants. Biochar was produced by slow pyrolysis at 300°C, and characterization was performed through microscopy, chemical, and structural composition analyses. Molecular and physiological analyses were performed in rice plants submitted to different biochar concentrations (0.02, 0.1, and 0.5 mg mL-1) to assess growth and productivity parameters. Morphological and physicochemical characterization revealed a heterogeneous morphology and the presence of several minerals (Na, K, P, Mg, Ca, S, Fe, and Si) in the biochar composition. Chemical modification of compounds post-pyrolysis and a highly porous structure with micropores were observed. Rice plants submitted to 0.5 mg mL-1 of biochar presented a decrease in root length, followed by an increase in root dry weight. The same concentration influenced seed production, with an increase of 44% in the number of seeds per plant, 17% in the percentage of full seeds per plant, 12% in the weight of 1,000 full seeds, 53% in the seed weight per plant, and 12% in grain area. Differential proteomic analyses in shoots and roots of rice plants submitted to 0.5 mg mL-1 of biochar for 20 days revealed a fine-tuning of resource allocation towards seed production. These results suggest that biochar derived from Arthrospira platensis biomass can stimulate rice seed production.

Comparative analysis of farmer practices and high yield experiments: Farmers could get more maize yield from maize-soybean relay intercropping through high density cultivation of maize

Intercropping is a high-yield, resource-efficient planting method. There is a large gap between actual yield and potential yield at farmer's field. Their actual yield of intercropped maize remains unclear under low solar radiation-area, whether this yield can be improved, and if so, what are the underlying mechanism for increasing yield? In the present study, we collected the field management and yield data of intercropping maize by conducting a survey comprising 300 farmer households in 2016-2017. Subsequently, based on surveyed data, we designed an experiment including a high density planting (Dense cultivation and high N fertilization with plough tillage; DC) and normal farmer practice (Common cultivation; CC) to analyze the yield, canopy structure, light interception, photosynthetic parameters, and photosynthetic productivity. Most farmers preferred rotary tillage with a low planting density and N fertilization. Survey data showed that farmer yield ranged between 4-6 Mg ha^-1, with highest yield recorded at 10-12 Mg ha^-1, suggesting a possibility for yield improvement by improved cropping practices. Results from high density experiment showed that the two-years average yield for DC was 28.8% higher than the CC. Compared to CC, the lower angle between stem and leaf (LA) and higher leaf area index (LAI) in DC resulted in higher light interception in middle canopy and increased the photosynthetic productivity under DC. Moreover, in upper and lower canopies, the average activity of phosphoenolpyruvate (PEP) carboxylase was 70% higher in DC than CC. Briefly, increase in LAI and high Pn improved both light interception and photosynthetic productivity, thereby mediating an increase in the maize yield. Overall, these results indicated that farmer's yields on average can be increased by 2.1 Mg ha^-1 by increasing planting density and N fertilization, under plough tillage.

A physiological and metabolomic analysis reveals the effect of shading intensity on blueberry fruit quality

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The FT1 shading treatment yielded the largest values for blueberry single fruit weight.

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The highest total phenol, anthocyanin and vitamin C contents under the FT1 shading treatment.

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470 known metabolites were obtained from blueberry fruits.

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This study provides scientific basis for improving the quality of blueberry fruit.

With the advancement of blueberry industrialization, cultivation measures for obtaining high-quality fruits and technologies for obtaining high levels of the main secondary metabolites have become inevitable requirements for further development of the blueberry industry. This study applied different shading treatments and found that the FT1 shading treatment yielded the largest values for the single fruit weight, solid longitudinal diameter and transverse diameter of blueberry fruit as well as the highest solidity-acid ratio and total phenol and vitamin C contents. Moreover, 470 known metabolites were obtained from blueberry fruits. Interestingly, the differentially abundant metabolites related to ABC transporters, pyrimidine metabolism, and purine metabolism pathways were commonly identified from the three comparisons, which indicated that these three metabolic pathways in blueberry fruits are vulnerable to shading treatment. This study provides a theoretical basis for the application of summer shading to improve the quality and antioxidant substances of small berries.

Genetic control of leaf angle in sorghum and its effect on light interception

Developing sorghum genotypes adapted to different light environments requires understanding of a plant's ability to capture light, determined through leaf angle specifically. This study dissected the genetic basis of leaf angle in 3 year field trials at two sites, using a sorghum diversity panel (729 accessions). A wide range of variation in leaf angle with medium heritability was observed. Leaf angle explained 36% variation in canopy light extinction coefficient, highlighting the extent to which variation in leaf angle influences light interception at the whole-canopy level. This study also found that the sorghum races of Guinea and Durra consistently having the largest and smallest leaf angle, respectively, highlighting the potential role of leaf angle in adaptation to distinct environments. The genome-wide association study detected 33 quantitative trait loci (QTLs) associated with leaf angle. Strong synteny was observed with previously detected leaf angle QTLs in maize (70%) and rice (40%) within 10 cM, among which the overlap was significantly enriched according to χ2 tests, suggesting a highly consistent genetic control in grasses. A priori leaf angle candidate genes identified in maize and rice were found to be enriched within a 1-cM window around the sorghum leaf angle QTLs. Additionally, protein domain analysis identified the WD40 protein domain as being enriched within a 1-cM window around the QTLs. These outcomes show that there is sufficient heritability and natural variation in the angle of upper leaves in sorghum which may be exploited to change light interception and optimize crop canopies for different contexts.

Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification

The automatic segmentation of ears in wheat canopy images is an important step to measure ear density or extract relevant plant traits separately for the different organs. Recent deep learning algorithms appear as promising tools to accurately detect ears in a wide diversity of conditions. However, they remain complicated to implement and necessitate a huge training database. This paper is aimed at proposing an easy and quick to train and robust alternative to segment wheat ears from heading to maturity growth stage. The tested method was based on superpixel classification exploiting features from RGB and multispectral cameras. Three classifiers were trained with wheat images acquired from heading to maturity on two cultivars at different levels of fertilizer. The best classifier, the support vector machine (SVM), yielded satisfactory segmentation and reached 94% accuracy. However, the segmentation at the pixel level could not be assessed only by the superpixel classification accuracy. For this reason, a second assessment method was proposed to consider the entire process. A simple graphical tool was developed to annotate pixels. The strategy was to annotate a few pixels per image to be able to quickly annotate the entire image set, and thus account for very diverse conditions. Results showed a lesser segmentation score (F1-score) for the heading and flowering stages and for the zero nitrogen input object. The methodology appeared appropriate for further work on the growth dynamics of the different wheat organs and in the frame of other segmentation challenges.