Gossypium raimondii (2n=2x=26, D₅), an untapped wild species, is the putative progenitor of the D-subgenome of G. hirsutum (2n=4x=52, AD₁), an extensively cultivated species.  Here, we developed a G. hirsutum (recipient)–G. raimondii (donor) introgression population to exploit the favorable QTLs/genes and mapped potential quantitative trait loci (QTLs) from wild cotton species.  The introgression population consisted of 256 lines with an introgression rate of 52.33% for the G. raimondii genome.  The introgression segment length range was 0.03–19.12 Mb, with an average of 1.22 Mb.  The coverage of total introgression fragments from G. raimondii was 386.98 Mb.  Further genome-wide association analysis (Q+K+MLM) and QTL mapping (RSTEP-LRT) identified 59 common QTLs, including 14 stable QTLs and six common QTL (co-QTL) clusters, and one hotspot of micronaire (MIC).  The common QTLs for seed index all showed positive additive effects, while the common QTLs for boll weight all had negative additive effects, indicating that the linkage between seed index and boll weight could be broken.  QTLs for lint percentage showed positive effects and could be beneficial for improving cotton yield.  Most QTLs for fiber quality had negative additive effects, implying these QTLs were domesticated/improved in G. hirsutum.  A few fiber quality QTLs showed positive additive effects, so they could be used to improve cotton fiber quality.  The introgression lines developed could be useful for molecular marker-assisted breeding and mapping QTLs precisely for mining desirable genes from the wild species G. raimondii.  Such genes can improve cultivated cotton in the future through a design-breeding approach. 

Potassium (K) is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution.  This indirectly leads to variations in the leaf potassium content (LKC, %) at different leaf positions.  However, owing to the interaction between light and leaf age, leaf sensitivity at different positions to this change varies, including the reflection and absorption of the spectrum.  How to selecting the optimal monitoring leaf position is an important factor in quickly and accurately evaluation of cotton LKC using spectral remote sensing technology.  Therefore, this study proposes a comprehensive multileaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom.  This is aimed at achieving an accurate estimation of cotton LKC and optimizing the strategy for selecting the monitored leaf position. Between 2020 and 2021, we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom (Li, i=1, 2, 3, ... , n), during the cotton budding, flowering, and boll setting stages.  Vertical distribution characteristics, sensitivity differences, and spectral correlations of LKC at different leaf positions were investigated.  Additionally, the optimal range of the dominant leaf position for monitoring was determined.  Partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and the entropy weight method (EWM) were used to establish LKC estimation models for single leaf and multileaf positions.  The results showed a vertical heterogeneous distribution of cotton LKC, with LKC initially increasing and then gradually decreasing from top to bottom, and the average LKC of cotton reaches its maximum value at flowering stage.  The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum.  The selected dominant leaf positions for the three growth stages were L1–L5, L1–L4, and L1–L2, respectively.  Based on the dominant leaf position monitoring range, the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4, PLSR-L1, and SVR-L2, with The coefficient of determination of the validation set (R²val) of 0.786, 0.580, and 0.768, and the root-mean-square error of the validation set (RMSEval) of 0.168, 0.197, and 0.191, respectively.  The multileaf position LKC estimation model was constructed by EWM with R²val of 0.887, 0.728, and 0.703, and RMSEval of 0.134, 0.172, and 0.209, respectively.  In contrast, the newly developed multileaf position comprehensive estimation model yielded superior results, improving the stability of the model on the basis of high accuracy, especially during the budding and flowering stages.  These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.

Cropland nitrate leaching is the major nitrogen (N) loss pathway, and it contributes significantly to water pollution. However, cropland nitrate leaching estimates show great uncertainty due to variations in input datasets and estimation methods. Here, we presented a re-evaluation of Chinese cropland nitrate leaching, and identified and quantified the sources of uncertainty by integrating three cropland area datasets, three N input datasets, and three estimation methods. The results revealed that nitrate leaching from Chinese cropland averaged 6.7±0.6 Tg N yr⁻¹ in 2010, ranging from 2.9 to 15.8 Tg N yr⁻¹ across 27 different estimates. The primary contributor to the uncertainty was the estimation method, accounting for 45.1%, followed by the interaction of N input dataset and estimation method at 24.4%. The results of this study emphasize the need for adopting a robust estimation method and improving the compatibility between the estimation method and N input dataset to effectively reduce uncertainty. This analysis provides valuable insights for accurately estimating cropland nitrate leaching and contributes to ongoing efforts that address water pollution concerns.

The plant circadian clock temporally drives gene expression through the day and coordinates various physiological process with diurnal environmental changes. It is essential to confer plant fitness and competitive advantage to survive and thrive under natural condition by circadian control of gene transcription. Chinese cabbage (Brassica rapa ssp. pekinensis) is an economically important vegetable crop worldwide; however, there is little information concerning its circadian clock system. Here we uncovered that gene expression patterns were affected by circadian oscillators at both transcriptional and post-transcriptional levels in Chinese cabbage. Time-course RNA-seq analyses were conducted on two short-period lines (SPcc-1 and SPcc-2) and two long-period lines (LPcc-1 and LPcc-2) under constant light. We showed that 32.7-50.5% of the genes were regulated by the circadian oscillator and the expression peak of cycling genes appeared even earlier in short-period lines compared to long-period lines. In addition, approximately 250 splicing events showed circadian regulation, of which intron retention (IR) accounted for a large proportion. Rhythmically spliced genes included the clock genes LATE ELONGATED HYOCOTYL (BrLHY), REVEILLE 2 (BrRVE2) and EARLY FLOWERING 3 (BrELF3). We also found that the circadian oscillator could notably influence the diurnal expression patterns of genes that are associated with glucose metabolism via photosynthesis, Calvin cycle and tricarboxylic acid (TCA) cycle at both transcriptional and post-transcriptional levels. Taken together, our results demonstrate that circadian-regulated physiological processes contribute to Chinese cabbage growth and development.