Improvement of the estimation of the infiltration function in surface irrigation systems.

Surface irrigation systems are widely used on the North China Plain. The design of surface irrigation systems can be improved by developing simulation models including the advanced trajectory, recession trajectory, and infiltration time. Therefore, the objectives of this study were as follows: (1) to evaluate different models to simulate the advanced and recession trajectories, (2) to propose a new method that reduces the required observation data for estimating the infiltration time, and (3) to evaluate the accuracy of the proposed infiltration function based on the modified infiltration time function. Field experiments were conducted. The results indicated that the power function can represent the advanced and recession trajectories well. A modified function that describes the infiltration time has a high correlation and accuracy with the measured data and can be used to estimate the infiltration time. The proposed infiltration function based on the modified infiltration time function is accurate and can be used to estimate the infiltration function.

Efficient Bayesian inference under the multispecies coalescent with migration.

Analyses of genome sequence data have revealed pervasive interspecific gene flow and enriched our understanding of the role of gene flow in speciation and adaptation. Inference of gene flow using genomic data requires powerful statistical methods. Yet current likelihood-based methods involve heavy computation and are feasible for small datasets only. Here, we implement the multispecies-coalescent-with-migration model in the Bayesian program bpp, which can be used to test for gene flow and estimate migration rates, as well as species divergence times and population sizes. We develop Markov chain Monte Carlo algorithms for efficient sampling from the posterior, enabling the analysis of genome-scale datasets with thousands of loci. Implementation of both introgression and migration models in the same program allows us to test whether gene flow occurred continuously over time or in pulses. Analyses of genomic data from Anopheles mosquitoes demonstrate rich information in typical genomic datasets about the mode and rate of gene flow.

Corrigendum: Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods.

[This corrects the article DOI: 10.3389/fpls.2022.1088499.].

Estimation of species divergence times in presence of cross-species gene flow.

Cross-species introgression can have significant impacts on phylogenomic reconstruction of species divergence events. Here, we used simulations to show how the presence of even a small amount of introgression can bias divergence time estimates when gene flow is ignored in the analysis. Using advances in analytical methods under the multispecies coalescent (MSC) model, we demonstrate that by accounting for incomplete lineage sorting and introgression using large phylogenomic data sets this problem can be avoided. The multispecies-coalescent-with-introgression (MSci) model is capable of accurately estimating both divergence times and ancestral effective population sizes, even when only a single diploid individual per species is sampled. We characterize some general expectations for biases in divergence time estimation under three different scenarios: 1) introgression between sister species, 2) introgression between non-sister species, and 3) introgression from an unsampled (i.e., ghost) outgroup lineage. We also conducted simulations under the isolation-with-migration (IM) model and found that the MSci model assuming episodic gene flow was able to accurately estimate species divergence times despite high levels of continuous gene flow. We estimated divergence times under the MSC and MSci models from two published empirical datasets with previous evidence of introgression, one of 372 target-enrichment loci from baobabs (Adansonia), and another of 1000 transcriptome loci from 14 species of the tomato relative, Jaltomata. The empirical analyses not only confirm our findings from simulations, demonstrating that the MSci model can reliably estimate divergence times but also show that divergence time estimation under the MSC can be robust to the presence of small amounts of introgression in empirical datasets with extensive taxon sampling. [divergence time; gene flow; hybridization; introgression; MSci model; multispecies coalescent].

Response of soil nutrients retention and rice growth to biochar in straw returning paddy fields.

Applying straw to agricultural production to improve soil productivity and crop yields is significant. However, the straw-only application is possibly not a practical choice for achieving environmental protection and high yield. This study evaluated the applicability of straw combined with biochar to the paddy field. Two-year pot experiments were conducted to examine the effect of straw combined with different proportions (0, 5, 20, 40 t ha-1) of biochar on soil nitrogen retention, phosphorous availability, rice yield, and physiological parameters. Five treatments were included: control (CK), 7 t ha-1 straw + 0 t ha-1 biochar (ST), 7 t ha-1 straw + 5 t ha-1 biochar (SC1), 7 t ha-1 straw + 20 t ha-1 biochar (SC2), 7 t ha-1 straw + 40 t ha-1 biochar (SC3). The results indicated that the biochar had an encouraging effect on paddy fields with straw returning: (1) SC3 treatment significantly increased ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3--N) content in soils compared to ST, increasing by 30.19% and 42.72%, while SC2 treatment increased by 25.84% and 30.40%, respectively; (2) Regarding soil phosphorus availability, ST treatment showed a negative effect, while proper biochar application rate (20 t ha-1) effectively increased Olsen-P content (18.24%); (3) No significant difference among these treatments was observed in the photosynthetic characteristics. Notably, 20 t ha-1 biochar application (SC2) effectively enhanced rice components (stem, ear) dry biomass, improved rice yield (10.14%), and Harvest index (HI: 4.99%). Hence, the appropriate rate (20 t ha-1) of biochar combined with straw (7 t ha-1) returning is a promising strategy for increasing nitrogen retention and phosphorous availability, alleviating N and P losses and promoting rice growth and yield. These findings are expected to provide a new perspective in that straw-returning with biochar achieves high efficiency, ecological, and sustainable development of agriculture.

High-resolution crop yield and water productivity dataset generated using random forest and remote sensing.

Accurate and high-resolution crop yield and crop water productivity (CWP) datasets are required to understand and predict spatiotemporal variation in agricultural production capacity; however, datasets for maize and wheat, two key staple dryland crops in China, are currently lacking. In this study, we generated and evaluated a long-term data series, at 1-km resolution of crop yield and CWP for maize and wheat across China, based on the multiple remotely sensed indicators and random forest algorithm. Results showed that MOD16 products are an accurate alternative to eddy covariance flux tower data to describe crop evapotranspiration (maize and wheat RMSE: 4.42 and 3.81 mm/8d, respectively) and the proposed yield estimation model showed accuracy at local (maize and wheat rRMSE: 26.81 and 21.80%, respectively) and regional (maize and wheat rRMSE: 15.36 and 17.17%, respectively) scales. Our analyses, which showed spatiotemporal patterns of maize and wheat yields and CWP across China, can be used to optimize agricultural production strategies in the context of maintaining food security.

Effect of Irrigation and Fertilizer Management on Rice Yield and Nitrogen Loss: A Meta-Analysis.

Irrigation and nitrogen fertilizer application are two important factors affecting yield and nitrogen loss in rice fields; however, the interaction effects of different irrigation schedules and combined management of nitrogen fertilizer application on yield and nitrogen loss in rice fields remain unknown. Therefore, we collected 327 sets of data on rice yield and 437 sets of data on nitrogen loss in rice fields from 2000 to 2021 and investigated the effects of different water-saving irrigation schedules, nitrogen application levels, and water-nitrogen couplings on rice yield, nitrogen use efficiency, and nitrogen loss (N2O emissions, nitrogen runoff, nitrogen leaching, and ammonia volatilization) by meta-analysis using conventional flooding irrigation and no nitrogen treatment as controls. The results showed that alternate wet and dry irrigation and controlled irrigation had increasing effects on rice yield. Alternate wet and dry irrigation had a significant yield-increasing effect (average 2.57% increase) and dry cultivation significantly reduced rice yield with an average 21.25% yield reduction. Water-saving irrigation reduces nitrogen runoff and leaching losses from rice fields but increases N2O emissions, and alternate wet and dry irrigation has a significant effect on increasing N2O emissions, with an average increase of 67.77%. Most water-saving irrigation can increase nitrogen use efficiency. Among water-saving irrigation methods, the effect of controlled irrigation on increasing nitrogen use efficiency is 1.06%. Rice yield and nitrogen use efficiency both showed a trend of increasing then decreasing with nitrogen fertilizer application, and nitrogen loss gradually increased with the amount of nitrogen fertilizer input. Water-nitrogen coupling management can significantly reduce nitrogen loss in rice fields while saving water and increasing yield. Based on the analysis of the data in this study, when the irrigation amount was 300~350 mm and the nitrogen application amount was 200~250 kg/ha, the rice yield and nitrogen fertilizer use efficiency were at a high level, which corresponded to the irrigation schedule of controlled irrigation or alternating wet and dry irrigation in the literature. However, different rice-growing areas are affected by rainfall and land capability, etc. Further optimization and correction of the adapted water and fertilizer management system for paddy fields are needed. The optimal water-nitrogen pattern of this study can achieve high rice yield and reduce nitrogen loss.

Bayesian Phylogenetic Inference using Relaxed-clocks and the Multispecies Coalescent.

The multispecies coalescent (MSC) model accommodates both species divergences and within-species coalescent and provides a natural framework for phylogenetic analysis of genomic data when the gene trees vary across the genome. The MSC model implemented in the program bpp assumes a molecular clock and the Jukes-Cantor model, and is suitable for analyzing genomic data from closely related species. Here we extend our implementation to more general substitution models and relaxed clocks to allow the rate to vary among species. The MSC-with-relaxed-clock model allows the estimation of species divergence times and ancestral population sizes using genomic sequences sampled from contemporary species when the strict clock assumption is violated, and provides a simulation framework for evaluating species tree estimation methods. We conducted simulations and analyzed two real datasets to evaluate the utility of the new models. We confirm that the clock-JC model is adequate for inference of shallow trees with closely related species, but it is important to account for clock violation for distant species. Our simulation suggests that there is valuable phylogenetic information in the gene-tree branch lengths even if the molecular clock assumption is seriously violated, and the relaxed-clock models implemented in bpp are able to extract such information. Our Markov chain Monte Carlo algorithms suffer from mixing problems when used for species tree estimation under the relaxed clock and we discuss possible improvements. We conclude that the new models are currently most effective for estimating population parameters such as species divergence times when the species tree is fixed.

A quantitative review of the effects of biochar application on rice yield and nitrogen use efficiency in paddy fields: A meta-analysis.

Applying biochar to paddy fields is a helpful approach that potentially increases rice production and nitrogen use efficiency (NUE) to ensure food security and protect the ecological environment. Notwithstanding, reviewing most of the previous experimental studies on the impacts of biochar reveals a considerable inconsistency in the proposed results. The present study conducts a comprehensive meta-analysis on the literature published before February 2021 to investigate the impacts of biochar properties, experimental conditions, and soil properties on rice yield and NUE. The meta-analysis results show that biochar application increases rice yield and NUE by 10.73% and 12.04%, respectively. The most significant improvements in the soil properties are seen in alkaline soils and paddy soils with a fine-textured. In addition, the benefits of biochar are significantly enhanced when produced at 500-600 °C with livestock manure due to the existence of more nutrients compared to other feedstocks. Analysis of water management reveals that biochar application under water-saving irrigation is more effective in increasing rice productivity. In terms of application rates, the >20 t/ha biochar and 150-250 kg/ha nitrogen fertilizer are recommended for improving rice yield and NUE. Regardless of existing uncertainty due to the lack of long-term experimental data, those investigated factors have significant implications for biochar management strategies in rice growth systems.

Effects of biochar on water quality and rice productivity under straw returning condition in a rice-wheat rotation region.

Straw returning is helpful to improve soil properties and realize the reutilization of agricultural waste. However, wheat straw returning may result in paddy water quality deterioration in rice-wheat rotation regions. This study conducted pot experiments of rice planting with different biochar application rates (0, 5, 20, and 40 t/hm2) under wheat straw returning conditions. The purposes are to investigate the applicability of biochar mixed with wheat straw returning to paddy fields and explore the effects of biochar on water quality, leaching losses of nitrogen (N) and phosphorus (P), and rice yield components. Results indicated that total straw returning reduced the water quality in paddy surface water and aggravated the leaching losses of N and P. Fortunately, the biochar application improved the negative effects caused by straw returning. 40 t/hm2 biochar mixed with straw returning significantly reduced the concentrations of COD and N in paddy surface water and N leaching loss than straw returning treatment (ST), decreased by 48.33%, 41.01%, and 45.73%, respectively. Meanwhile, applying biochar at a rate of 20 t/hm2 with straw returning is suitable to control the diffusion of P. In addition, the ST treatment had no significant effect on rice yield, while the proper application rate of biochar under straw returning condition can improve rice yield and promote N utilization. 20 t/hm2 biochar treatment is more effective to improving rice yield (16.89%) and N use efficiency (NUE) (10.14%). These findings can provide a new method to solve the negative effects of total straw returning on the water environment and rice growth and guide the utilization of straw resources in the rice-wheat rotation regions.

Retrieving rice (Oryza sativa L.) net photosynthetic rate from UAV multispectral images based on machine learning methods.

Photosynthesis is the key physiological activity in the process of crop growth and plays an irreplaceable role in carbon assimilation and yield formation. This study extracted rice (Oryza sativa L.) canopy reflectance based on the UAV multispectral images and analyzed the correlation between 25 vegetation indices (VIs), three textural indices (TIs), and net photosynthetic rate (Pn) at different growth stages. Linear regression (LR), support vector regression (SVR), gradient boosting decision tree (GBDT), random forest (RF), and multilayer perceptron neural network (MLP) models were employed for Pn estimation, and the modeling accuracy was compared under the input condition of VIs, VIs combined with TIs, and fusion of VIs and TIs with plant height (PH) and SPAD. The results showed that VIs and TIs generally had the relatively best correlation with Pn at the jointing-booting stage and the number of VIs with significant correlation (p< 0.05) was the largest. Therefore, the employed models could achieve the highest overall accuracy [coefficient of determination (R 2) of 0.383-0.938]. However, as the growth stage progressed, the correlation gradually weakened and resulted in accuracy decrease (R 2 of 0.258-0.928 and 0.125-0.863 at the heading-flowering and ripening stages, respectively). Among the tested models, GBDT and RF models could attain the best performance based on only VIs input (with R 2 ranging from 0.863 to 0.938 and from 0.815 to 0.872, respectively). Furthermore, the fusion input of VIs, TIs with PH, and SPAD could more effectively improve the model accuracy (R 2 increased by 0.049-0.249, 0.063-0.470, and 0.113-0.471, respectively, for three growth stages) compared with the input combination of VIs and TIs (R 2 increased by 0.015-0.090, 0.001-0.139, and 0.023-0.114). Therefore, the GBDT and RF model with fused input could be highly recommended for rice Pn estimation and the methods could also provide reference for Pn monitoring and further yield prediction at field scale.

Multispecies coalescent and its applications to infer species phylogenies and cross-species gene flow.

Multispecies coalescent (MSC) is the extension of the single-population coalescent model to multiple species. It integrates the phylogenetic process of species divergences and the population genetic process of coalescent, and provides a powerful framework for a number of inference problems using genomic sequence data from multiple species, including estimation of species divergence times and population sizes, estimation of species trees accommodating discordant gene trees, inference of cross-species gene flow and species delimitation. In this review, we introduce the major features of the MSC model, discuss full-likelihood and heuristic methods of species tree estimation and summarize recent methodological advances in inference of cross-species gene flow. We discuss the statistical and computational challenges in the field and research directions where breakthroughs may be likely in the next few years.

Defining Species When There is Gene Flow.

Whatever one's definition of species, it is generally expected that individuals of the same species should be genetically more similar to each other than they are to individuals of another species. Here, we show that in the presence of cross-species gene flow, this expectation may be incorrect. We use the multispecies coalescent model with continuous-time migration or episodic introgression to study the impact of gene flow on genetic differences within and between species and highlight a surprising but plausible scenario in which different population sizes and asymmetrical migration rates cause a genetic sequence to be on average more closely related to a sequence from another species than to a sequence from the same species. Our results highlight the extraordinary impact that even a small amount of gene flow may have on the genetic history of the species. We suggest that contrasting long-term migration rate and short-term hybridization rate, both of which can be estimated using genetic data, may be a powerful approach to detecting the presence of reproductive barriers and to define species boundaries.[Gene flow; introgression; migration; multispecies coalescent; species concept; species delimitation.].

Comparison of five Boosting-based models for estimating daily reference evapotranspiration with limited meteorological variables.

Accurate ET0 estimation is of great significance in effective agricultural water management and realizing future intelligent irrigation. This study compares the performance of five Boosting-based models, including Adaptive Boosting(ADA), Gradient Boosting Decision Tree(GBDT), Extreme Gradient Boosting(XGB), Light Gradient Boosting Decision Machine(LGB) and Gradient boosting with categorical features support(CAT), for estimating daily ET0 across 10 stations in the eastern monsoon zone of China. Six different input combinations and 10-fold cross validation method were considered for fully evaluating model accuracy and stability under the condition of limited meteorological variables input. Meanwhile, path analysis was used to analyze the effect of meteorological variables on daily ET0 and their contribution to the estimation results. The results indicated that CAT models could achieve the highest accuracy (with global average RMSE of 0.5667 mm d-1, MAE of 4199 mm d-1and Adj_R2 of 0.8514) and best stability regardless of input combination and stations. Among the inputted meteorological variables, solar radiation(Rs) offers the largest contribution (with average value of 0.7703) to the R2 value of the estimation results and its direct effect on ET0 increases (ranging 0.8654 to 0.9090) as the station's latitude goes down, while maximum temperature (Tmax) showes the contrary trend (ranging from 0.8598 to 0.5268). These results could help to optimize and simplify the variables contained in input combinations. The comparison between models based on the number of the day in a year (J) and extraterrestrial radiation (Ra) manifested that both J and Ra could improve the modeling accuracy and the improvement increased with the station's latitudes. However, models with J could achieve better accuracy than those with Ra. In conclusion, CAT models can be most recommended for estimating ET0 and input variable J can be promoted to improve model performance with limited meteorological variables in the eastern monsoon zone of China.

An improved approach to estimating the infiltration characteristics in surface irrigation systems.

The most common methods for estimating the infiltration function are measurements through a double-ring infiltrometer (DRI) and empirical models. Infiltration data always exhibit different kinds of scatter, which affect the accuracy of the estimated infiltration function. This study presents a new methodology to calibrate the infiltration function. The suggested approach is based on combining the DRI method with the changes in the measured soil water content. Furrow irrigation experiments were conducted to estimate the infiltration function using different methods and to investigate the effect of data scatter on the reliability of the estimated infiltration function. Furrow elevations were observed, and for each irrigation event advance times, recession times, and inflow rates were observed. The infiltration depths were measured as a function of the change in the soil water content before and after irrigation event. Infiltration parameters were estimated using DRI treatment, empirical model (Kostiakov model), and suggested approach. Measured and simulated infiltration depths using the described methods were compared. The results show that the infiltration depths estimated using a DRI were lower than the observed infiltration depths, while the infiltration depths estimated using the empirical model were higher than the observed infiltration depths. The results indicate that the infiltration function estimated using the recommended approach was more accurate and reasonable than the infiltration function estimated using the DRI, and empirical (Kostiakov model) methods. In addition, the proposed approach can reduce the required measurements during the irrigation event, and can also reduce the potential scatter in the estimated infiltration function that results from soil variability and measurement errors.

Improving border irrigation performance with predesigned varied-discharge.

Insufficient water resources restrict wheat production in the North China Plain, so it is urgent and essential to improve the border irrigation performance and water use efficiency. This study developed a predesigned varied-discharge irrigation scheme in the closed-ended border. Field treatments, including continuous-discharge (CD), increased-discharge (ID) and decreased-discharge (DD) border irrigation tests, were conducted to evaluate the irrigation performance of the proposed varied-discharge scheme. The DD border irrigation treatment had great application efficiency (AE), distribution uniformity (DU) and requirement efficiency (RE), and its comprehensive evaluation indicator (Y) was also significantly higher than other treatments. DD treatment achieved the average AE, DU, RE and Y values of 91.4%, 95.5%, 99.5% and 95.4%, respectively. Furthermore, the hydraulic simulation model WinSRFR was used to optimize the scheme of predesigned varied-discharge border irrigation, and sensitivity analyses of infiltration parameters, roughness coefficient, slope and inflow rate were carried out. The results indicate that the predesigned varied-discharge border irrigation scheme can improve the irrigation performance, and the DD border irrigation scheme has more satisfactory robustness than that of the ID border irrigation scheme.

The Impact of Cross-Species Gene Flow on Species Tree Estimation.

Recent analyses of genomic sequence data suggest cross-species gene flow is common in both plants and animals, posing challenges to species tree estimation. We examine the levels of gene flow needed to mislead species tree estimation with three species and either episodic introgressive hybridization or continuous migration between an outgroup and one ingroup species. Several species tree estimation methods are examined, including the majority-vote method based on the most common gene tree topology (with either the true or reconstructed gene trees used), the UPGMA method based on the average sequence distances (or average coalescent times) between species, and the full-likelihood method based on multilocus sequence data. Our results suggest that the majority-vote method based on gene tree topologies is more robust to gene flow than the UPGMA method based on coalescent times and both are more robust than likelihood assuming a multispecies coalescent (MSC) model with no cross-species gene flow. Comparison of the continuous migration model with the episodic introgression model suggests that a small amount of gene flow per generation can cause drastic changes to the genetic history of the species and mislead species tree methods, especially if the species diverged through radiative speciation events. Estimates of parameters under the MSC with gene flow suggest that African mosquito species in the Anopheles gambiae species complex constitute such an example of extreme impact of gene flow on species phylogeny. [IM; introgression; migration; MSci; multispecies coalescent; species tree.].

A Bayesian Implementation of the Multispecies Coalescent Model with Introgression for Phylogenomic Analysis.

Recent analyses suggest that cross-species gene flow or introgression is common in nature, especially during species divergences. Genomic sequence data can be used to infer introgression events and to estimate the timing and intensity of introgression, providing an important means to advance our understanding of the role of gene flow in speciation. Here, we implement the multispecies-coalescent-with-introgression model, an extension of the multispecies-coalescent model to incorporate introgression, in our Bayesian Markov chain Monte Carlo program Bpp. The multispecies-coalescent-with-introgression model accommodates deep coalescence (or incomplete lineage sorting) and introgression and provides a natural framework for inference using genomic sequence data. Computer simulation confirms the good statistical properties of the method, although hundreds or thousands of loci are typically needed to estimate introgression probabilities reliably. Reanalysis of data sets from the purple cone spruce confirms the hypothesis of homoploid hybrid speciation. We estimated the introgression probability using the genomic sequence data from six mosquito species in the Anopheles gambiae species complex, which varies considerably across the genome, likely driven by differential selection against introgressed alleles.

Effects of winter irrigation on soil salinity and jujube growth in arid regions.

The considerably high evapotranspiration and the low leaching fraction of the soil in arid regions are likely the primary causes of the enhanced soil salinity in such regions. Winter irrigation has proven to be very effective for promoting the leaching of salts from the rooting-zone. In this study, we investigated the effects of different irrigation methods (flood irrigation and drip irrigation) and winter irrigation quotas (450, 1350, 2250, 3150, 4050, and 4950 m3/hm2) on soil salinity and plant growth in an arid region. The sum of ECe in the 0-100 cm soil layer was 56.26-29.32 ms/cm under flood irrigation, 61.37-17.90 ms/cm under drip irrigation, and 64.13 ms/cm under no irrigation. The survival rates of jujube trees reached 65% and 77%, respectively, for drip irrigation and flood irrigation with a quota of 2250 m3/hm2. Furthermore, at irrigation quotas in excess of 3150 m3/hm2 the ground diameter and height of jujube trees were significantly greater than those observed under nonwinter irrigation and several other winter irrigation treatments. These findings indicated that winter irrigation significantly reduced soil salinity, changed the soil salt distribution, created a good environment for the growth of jujube trees and improved the survival rate of young jujube trees, especially under winter drip irrigation with a quota of 3150 m3/hm2. In addition, 1-year-old jujube trees emerging in spring may benefit from an ECe lower than 5 ms/cm.

Species Tree Inference with BPP Using Genomic Sequences and the Multispecies Coalescent.

The multispecies coalescent provides a natural framework for accommodating ancestral genetic polymorphism and coalescent processes that can cause different genomic regions to have different genealogical histories. The Bayesian program BPP includes a full-likelihood implementation of the multispecies coalescent, using transmodel Markov chain Monte Carlo to calculate the posterior probabilities of different species trees. BPP is suitable for analyzing multilocus sequence data sets and it accommodates the heterogeneity of gene trees (both the topology and branch lengths) among loci and gene tree uncertainties due to limited phylogenetic information at each locus. Here, we provide a practical guide to the use of BPP in species tree estimation. BPP is a command-line program that runs on linux, macosx, and windows. This protocol shows how to use both BPP 3.4 (http://abacus.gene.ucl.ac.uk/software/) and BPP 4.0 (https://github.com/bpp/).