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The Historic Square Foot Dataset – Outstanding small‐scale richness in Swiss grasslands around the year 1900

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The Historic Square Foot Dataset – Outstanding small-scale richness in Swiss grasslands around the year 1900

The Historic Square Foot Dataset comprises several hundred vegetation plots conducted between 1884 and 1931 in grassland habitats all over Switzerland. These data are unique in providing insights in how grasslands in Central Europe were composed, making them an important source of information for vegetation science and global-change studies. The digitised and georeferenced data are made accessible through this publication.


Abstract

Grasslands host a significant share of Europe's species diversity but are among the most threatened vegetation types of the continent. Resurvey studies can help to understand patterns and drivers of changes in grassland diversity and species composition. However, most resurveys are based on local or regional data, and hardly reach back more than eight decades. Here, we publish and describe the Historic Square Foot Dataset, comprising 580 0.09-m2 and 43 1-m2 vegetation plots carefully sampled between 1884 and 1931, covering a wide range of grassland types across Switzerland. We provide the plots as an open-access data set with coordinates, relocation accuracy and fractional aboveground biomass per vascular plant species. We assigned EUNIS habitat types to most plots. Mean vascular plant species richness in 0.09 m2 was 19.7, with a maximum of 47. This is considerably more than the present-day world record of 43 species for this plot size. Historically, species richness did not vary with elevation, differing from the unimodal relationship found today. The data set provides unique insight into how grasslands in Central Europe looked more than 100 years ago, thus offering manifold options for studies on the development of grassland biodiversity and productivity.

Combinatory ability and heterosis for quantitative traits related to productivity and the pungency in F1 hybrids of habanero pepper (Capsicum chinense Jacq.)

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Abstract

The objective of this work was to obtain high-yielding F1 hybrids of the habanero pepper, using 10 outstanding parents in a line-tester genetic design. General combinatorial ability and specific combinatorial ability were evaluated in the parents' and the hybrids F1 obtained, respectively. Heterosis was determined in the 18 hybrids obtained. The technique of HPLC was used to evaluate the capsaicin content in the fruit of 10 parents and 18 hybrids of habanero pepper. The DNA profiles were analysed as part of the characterization of the germplasm of the species conserved at the CICY. Furthermore, the molecular variation of the genotypes under investigation was assessed using eight SSR and nine ISSR markers. The results showed the presence of substantial morphoagronomic and molecular variability among the habanero pepper genotypes evaluated. Genetic similarities of 83%–93% between parents and 76%–94% between hybrids were found. The most productive hybrids were H8, H10 and H19 with 3.13 to 4.29 kg/plant, respectively, these came from crosses where RNJ-04 (P26) as the male parent and the hybrid H60 (4.92 kg/plant) that comes from the male parent RES-08 (P30). Likewise, the H7 hybrid had the highest capsaicin content (128.41 mg/g dry weight, 960,687.00 SHU). Hybrids H43 with 114.39 mg/g DW and 90,444.30 SHU, and H51 with 11.61 mg/g DW and 934,745.07 SHU respectively, also stood out.

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits

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Abstract

Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such, genomic prediction in sugarcane presents an interesting case for machine learning (ML) methods, which are purportedly able to deal with high levels of complexity in prediction. Here, we investigated deep learning (DL) neural networks, including multilayer networks (MLP) and convolution neural networks (CNN), and an ensemble machine learning approach, random forest (RF), for genomic prediction in sugarcane. The data set used was 2912 sugarcane clones, scored for 26,086 genome wide single nucleotide polymorphism markers, with final assessment trial data for total cane harvested (TCH), commercial cane sugar (CCS), and fiber content (Fiber). The clones in the latest trial (2017) were used as a validation set. We compared prediction accuracy of these methods to genomic best linear unbiased prediction (GBLUP) extended to include dominance and epistatic effects. The prediction accuracies from GBLUP models were up to 0.37 for TCH, 0.43 for CCS, and 0.48 for Fiber, while the optimized ML models had prediction accuracies of 0.35 for TCH, 0.38 for CCS, and 0.48 for Fiber. Both RF and DL neural network models have comparable predictive ability with the additive GBLUP model but are less accurate than the extended GBLUP model.

Separate or combined effects of soil compaction and/or drought on gas exchange, chlorophyll fluorescence and physiological traits of maize (Zea mays L.) hybrids

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Abstract

In the natural environment, plants are subjected to simultaneous or sequential presence of various abiotic and/or biotic stresses, including soil compaction and soil drought. The effects of these stresses tested separately are relatively well understood, but still little is known about their simultaneous effects on plants. Our research involved four single hybrids of maize differing in their degree of susceptibility to soil compaction and drought. We investigated the effects of low and high soil compaction under optimal irrigation (LI, HI) and under three-week long soil drought (LD, HD), on the gas exchange (Pn, E, gS, Ci) and chlorophyll fluorescence parameters (F 0, F m, F v, F v/F m), total leaf area (LA), leaf greening (SPAD), leaf water deficit (WD), leaf water potential (ψ) and membrane injury (MI). The plants experiencing high soil compaction (HI) showed a decrease in all parameters of gas exchange (Pn, E, gS, Ci), leaf area (LA), leaf greening (SPAD) and the maximal quantum efficiency of PSII (F v/F m) in comparison with plants growing in non-compacted soil (LI). An increase was observed in the other fluorescence parameters, i.e., F 0, F m and F v and leaf WD, ψ and MI in HI vs. LI variants. In the plants exposed to drought (LD, HD), the changes in the measured traits were greater, especially for the sensitive hybrids P-8400 and NS-3023, than for the plants from LI treatment. A significant interaction between the degree of stress susceptibility and relative trait change was observed for practically all of the measured features. Moreover, in the short recovery period after the end of drought, the measured traits in LD and HD plants did not fully return to the control level, especially in the case of the sensitive hybrids (P-8400 NS-3023). The physiological reaction of maize hybrids to soil compaction and/or soil drought indicated the genetically determined variability of tolerance to those stresses. Significant correlation between RTC and stress susceptibility indexes (S-SI) provided suitable criteria for the hybrid selection. Also, our results showed the plasticity and capability of maize hybrids to respond to environmental conditions.

Transmission of photosynthetically active radiation and the productivities of soybean and maize in agroforestry systems

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Abstract

In integrated crop-livestock-forestry (ICLF) systems, an agroforestry model, the forestry component influences the distribution of photosynthetically active radiation (PAR) and alters grain productivity. The aim of this study was to evaluate the effects of systematic and selective thinning of eucalyptus stands on the productivity of soybean and maize grown. The randomized block-designed experiment comprised three treatments, namely crops grown under full sunlight (CFS) and in ICLF plots subjected to systematic and selective thinning to leave single-row (ICLFS) and triple-row (ICLFT) tree configurations. Soybean and maize were planted in succession between the tree stands and PAR incidence/transmittance and crop productivities were evaluated in the north and south sun-exposed faces of the plots during three cultivation cycles after thinning. In comparison with CFS, PAR transmittance in the ICLF systems decrease varied according to the time of day, period of the year, distance of the sampling position from the trees, the sun-exposed area (north and south faces) and time after thinning. Soybean productivities in ICLFS were similar to those of CFS in the first and second crop cycles, but significantly lower (p < 0.05) in the third crop cycle. In the ICLFT system, soybean productivity was similar to that of CFS in the second crop cycle but significantly lower in the first and third crop cycles. With regard to the maize crop, productivities decreased in the order CSF > ICLFS > ICLFT in all cultivation cycles. Our results showed that PAR transmittance and soybean/maize productivities were positively influenced by thinning of eucalyptus stands, particularly in ICLFS system.

Screening of barley germplasm for drought tolerance based on root architecture, agronomic traits and identification of novel allelic variants of HVA1

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Abstract

Drought is a major constraint for barley production as it is normally cultivated in rainfed and marginal areas lacking optimum productivity. The domestication bottleneck and further selection pressure have resulted in reduced genetic diversity in barley. Genebank germplasm holds a huge potential for identifying new alleles for stress tolerance. In the present study, a diverse set of 214 accessions from Indian National Genebank were screened for drought tolerance in hydroponics and field conditions. Analysis of variance revealed a significant effect of drought on root architecture, relative water content, membrane stability index, chlorophyll content, plant height, and yield attributes. Cumulative stress response in terms of better root phenotype, physiological and agronomic traits showed accessions IC113045, EC578521, IC582699, EC492318, EC578711, EC667420, IC393980 and IC594943 as most promising donors for breeding programmes in drought-prone areas. Further allelic variation of candidate gene, Hordeum vulgare aleurone 1 (HVA1), and its promoter sequence was studied in a subset of drought-tolerant and -susceptible accessions. The HVA1 gene showed six SNPs and one indel in the genic regions whereas three SNPs and one indel in promoter. Two alleles of HVA1 gene, one in exotic and other in indigenous accession, were found to be associated with drought tolerance. These results were confirmed by qRT-PCR analysis exhibiting significant increase in transcript abundance of HVA1 in drought-tolerant accessions in comparison with susceptible accessions, thereby highlighting its possible role in imparting drought tolerance. The study helped identify genetic resources for drought tolerance in barley and unravelled new alleles of HVA1.

Classification of soybean genotypes during the seedling stage in controlled drought and salt stress environments using the decision tree algorithm

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Abstract

Soybean is one of the most important oilseed crops grown worldwide. However, abiotic stresses such as drought and salinity can seriously affect soybean production, especially in tropical climate conditions. To evaluate the adaptability and stability of soybean genotypes under abiotic stress conditions, some studies have proposed a multitrait tool to select stress-tolerant soybean genotypes through a multitrait stability index (MTSI). This index can be used under stressful environmental conditions to quantify the genotypic stability of soybean cultivars. Our study is based on an unprecedented approach, where we propose to use a machine learning algorithm called ‘Random Forest’ to obtain a classification model based on a decision tree algorithm. The decision tree data structure can be used even by nonexperts facilitating the decision-making process for genotype selection. The proposed model evaluated the importance of six shoot and root morphological variables and predicted from which controlled growth environment the soybean plants originated. Using this model more than 73% of the genotypic patterns were learned correctly. Besides that, this model can also predict and rank the most critical variables in the development of soybean genotypes, having obtained results very similar to recent field research. The research is important for plant breeders who seek an early selection of soybean seedlings for drought and saline stresses.

Relationship between characteristics of basal internodes and lodging and its physiological mechanism in direct‐seeded rice

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Abstract

Lodging is an important factor that limits rice yield and the large-scale promotion of direct-seeded rice (DSR). The objective of this study was to clarify the relationship between the characteristics of basal internodes and lodging and the physiological mechanism underlying this process in DSR. A field study was conducted in Changchun, Jilin Province, China, using a japonica rice variety Jiyujing with two direct seeding cultivation methods, including dry DSR (DDSR), wet DSR (WDSR) and conventional-transplanted rice (CTSR) as a control in 2019 and 2020. Lodging-related physical parameters, morphological characteristics and carbohydrate components of basal internodes were investigated at heading stage (HS) and 30 days after heading stage (HS30). The results showed that WDSR increased lodging index (LI) and lodging rate compared with CTSR and DDSR. LI increased rapidly from HS to HS30, primarily because of the significant reduction in the breaking strength (M). Correlation analysis revealed that the M of N4 internode was significantly positively correlated with culm plumpness and structural carbohydrate proportions at HS30. Culm plumpness decreased significantly, due to a decrease in nonstructural carbohydrate (NSC) content then primarily the decrease in starch proportions and content. Compared with CTSR and DDSR, WDSR decreased culm wall thickness, dry weight per centimetre of the culm and leaf sheaths, and the proportions and contents of cellulose, lignin and starch of internode, resulting in the decrease in internode breaking strength. Thus, it was concluded that the DSR reduced internode strength by reducing internode plumpness and carbohydrate content, thus decreasing lodging resistance.

Saline stress affects the growth of Saccharum complex genotypes

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Abstract

Soil salinity affects plant growth, compromising sugarcane cultivation in regions with great production potential. Saccharum complex genotypes that respond positively to growth under saline environment can be used in the diversification of sugarcane cultivars to obtain greater economic returns. The objective of this study was to evaluate growth-related traits of Saccharum genotypes grown under the presence and absence of salinity. The experiment was carried out in a 32 × 2 factorial scheme in a randomized block design with three replicates. The first factor consisted of 32 genotypes of the Saccharum complex and the second factor consisted of the presence and absence of salinity. The salinity provided higher mean values than the environment without salinity for plant height in the genotypes G9, G11, G13, G22 and G28, leaf number for G9 and G24, leaf area index for G9 and stem diameter for G1, G11 and G24. Among the genotypes tested, G1, G9, G11, G13, G22, G24 and G28 were the most promising genotypes and could be used for breeding new sugarcane cultivars of enhanced salinity tolerance.

Plant photosynthetic responses under drought stress: Effects and management

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Abstract

Balanced photosynthesis is essential for improved plant survival and agricultural benefits in terms of biomass and yield. Photosynthesis is the hub of energy metabolism in plants; however, drought stress (DS) strongly perturbs photosynthetic efficiency due to biochemical and diffusive limitations that reduce key photosynthetic components and close stomata. This review describes photosynthetic responses, chloroplast retrograde signalling, and genetic imprints that curtail DS damage to photosynthetic machinery. While stomatal closure, disrupted photosynthetic systems, over-reduced electron transport rates (ETR), partial hindrance of the Calvin cycle, and reduced pigment contents strongly affect the repertoire of photosynthetic processes under DS, chloroplast retrograde signalling also has a plausible role in preserving photosynthetic capacity. Progress in agronomic, genetic engineering approaches and isoprene regulation would help to rescue photosynthetic apparatus under DS.