Background Barley is one of the strategic agricultural products available in the world, and yield prediction is important for ensuring food security. One way of estimating a product is to use remote sensing data in conjunction with field data and meteorological data. One of the main issues surrounding this comprises the use of machine learning techniques to create a multi-resource data-based estimation model. Many studies have been conducted on barley yield prediction from planting to harvest. Still, the effect of different time intervals on yield prediction has not been investigated. Furthermore, the effect of different periods on yield prediction has not been investigated. Results In the present study, the whole growth period was divided into three parts. Using one of the major barley production areas in Iran, the performance of the proposed model was evaluated. In the first step, a model for integrating field data, remote sensing data and meteorological data was prepared. The results obtained show that, among the four machine learning methods implemented, the gaussian process regression algorithm performed best and estimated yield with r2 = 0.84, root mean square error = 737 kg ha-1 and mean absolute = 650 kg ha-1 , 1 month before harvest. Conclusion It was found that the estimation results change depending on different agricultural zones and temporal training settings. The findings of the present study provide a powerful potential tool for the yield prediction of barley using multi-source data and machine learning. © 2020 Society of Chemical Industry.

Yield prediction with machine learning algorithms and satellite images.

Biodiesel synthesis from Styrax officinalis L. seed oil as a novel and potential non-edible feedstock: A parametric optimization study through the Taguchi technique

The present study deals with the synthesis, characterization, physical and thermodynamic properties of a novel anionic surfactant derived from Sapindus laurifolius for its potential application against conventional non-biodegradable surfactants. The synthesized surfactant was characterized by FTIR, GC-MS, EDX and FE-SEM analyses. The surfactant showed good thermal stability at different temperatures as obtained from TGA studies. Critical micelle concentration (CMC) values were obtained by surface tensiometry measurements. DLS studies revealed the micelle structures of the CMC aggregates at higher concentrations. Low interfacial tension values were obtained at the oil–aqueous interfaces for surfactant solutions. The effect of temperature on the interfacial behaviour was also investigated. Thermodynamic studies showed that adsorption was more favoured in comparison to micellization for all systems. Foam stability studies were performed as a function of time and concentration by the Bartsch method. The surfactant also formed stable emulsions at concentrations near the CMC value. A comprehensive assessment of the thermal, interfacial, foaming and emulsifying properties of the soap-nut-based surfactant provides grounds for potential application in a wide range of industries.

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Synthesis, characterization, physical and thermodynamic properties of a novel anionic surfactant derived from Sapindus laurifolius

Synthesis and performance characterization of an efficient environmental-friendly Sapindus mukorossi saponins based hybrid coal dust suppressant

Geographical variations in the fatty acids of Zanthoxylum seed oils: A chemometric classification based on the random forest algorithm

Natural variation in fatty acid composition of Sapindus spp. seed oils

Sapindus mukorossi Gaertn and S delavayi Franchet are among the most valuable species in the genus Sapindus for their commercially exploitable plant oils and chemicals However, few studies have addressed genetic variation and improvement for either species We evaluated the genetic diversity of germplasm from selected plus trees within a wide region and established the relationship between fruit traits and molecular markers An association analysis based on inter-simple sequence repeats (ISSRs) provided a genetic basis for studies of fruit traits A total of 247 loci were detected by scanning 61 trees of S mukorossi and S delavayi using 16 ISSR markers Genetic diversity parameters were estimated for selected superior trees (or germplasm) and S mukorossi and S delavayi were categorized into two main groups, as well as into four groups within S mukorossi An association analysis between the ISSR markers and 14 fruit traits used the TASSEL MLM model A genetic structure analysis differentiated S mukorossi and S delavayi Eighteen ISSR loci associated with 13 fruit traits (P < 0005) were identified, with 13, 1, and 4 loci associated with seed oil production, fruit saponin production, and fruit quality, respectively Using this information, a core collection was selected with adequate genetic diversity and good seed oil characters Our results demonstrate the feasibility of effectively estimating fruit trait associations in Sapindus using ISSR markers, and the method is applicable and valuable for select germplasm conservation The markers obtained in this study are potentially useful for molecular-assisted breeding of Sapindus spp

Genetic diversity and association analyses of fruit traits with microsatellite ISSRs in Sapindus

Genetic structure and biogeographic divergence among Sapindus species: An inter-simple sequence repeat-based study of germplasms in China

Sapindus mukorossi is widely distributed in tropical and subtropical areas of southern China; the seed kernel oil is potential biodiesel material, and the saponins extracted from fruit pericarp are very valuable efficient natural surfactants. Therefore, S. mukorossi is an ideal tree species for developing forestry bioenergy and multiple other products. In this study, 42 S. mukorossi fruits from mother trees were collected from 39 distinct locations in 12 Chinese provinces to infer fruit and seed trait responses to environmental factors. Canonical correspondence analysis (CCA) was conducted using 21 horticultural fruit traits and 10 environmental factors that represented different climatic and geographic conditions throughout southern China. CCA revealed well-developed patterns of natural phenotypic variation, and insight into the ecological factors that are potentially important in shaping this variation. The results presented here further elucidate the natural distribution and ecological adaptations of wild S. mukorossi resources, which will be valuable for S. mukorossi cultivation by helping identify ideal planting areas. The germplasm resources with extensive morphological variation can also contribute to S. mukorossi breeding in the future by helping develop new cultivars with high saponin yield.

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Association of Fruit and Seed Traits of Sapindus mukorossi Germplasm with Environmental Factors in Southern China

Canopy characteristics and light distribution in Sapindus mukorossi Gaertn. are influenced by crown architecture manipulation in the hilly terrain of Southeast China.

Xuehuang Weng

Papers

Natural variation in fatty acid composition of Sapindus spp. seed oils

Genetic diversity and association analyses of fruit traits with microsatellite ISSRs in Sapindus

Genetic structure and biogeographic divergence among Sapindus species: An inter-simple sequence repeat-based study of germplasms in China

Association of Fruit and Seed Traits of Sapindus mukorossi Germplasm with Environmental Factors in Southern China

Canopy characteristics and light distribution in Sapindus mukorossi Gaertn. are influenced by crown architecture manipulation in the hilly terrain of Southeast China.