Main Growth Stages Research Articles

Effects of different phosphorus and potassium supply on the root architecture, phosphorus and potassium uptake, and utilization efficiency of hydroponic rice

Phosphorus (P) and potassium (K) affect seedling growth, root configuration, and nutrient uptake in hydroponic rice, but there are few studies on all growth stages of rice. The purpose of this experiment was to determine the response characteristics of root morphology, plant physiology, and P and K uptake and utilization efficiency to different supplies of P and K. Two local conventional rice varieties (Shennong 265 and Liaojing 294) were used as experimental materials across four treatments, including HPHK (sufficient P and K supply), HPLK (sufficient P supply under low K levels), LPHK (sufficient K supply under low P levels) and LPLK (low P and K levels) in a hydroponic setting. The results showed that HPHK and HPLK significantly decreased the acid phosphatase activity of leaves and roots from full heading to filling stages when compared to LPHK and LPLK. Sufficient supply of P or K significantly increased the accumulation of P and K (aboveground, leaves, stem sheath, and whole plant) and root morphological parameters (root length, root surface area, total root volume, and tips) during major growth stages when compared to LP or LK levels. HPHK was significantly higher than other treatments in terms of dry weight and the root activity at the main growth stage, P and K uptake rates in nutrient solutions at various stages, related P and K efficiency at the maturity stage, yield, effective panicle number, and grain number per panicle. In addition, the effect of HPHK on the above indexes were significantly greater than those of single sufficient supply of P or K. In conclusion, HPHK can improve plant configuration, increase plant P and K absorption and root activity, and increase rice yield and related P and K utilization efficiency.

Effects of Phosphorus and Potassium Supply on Photosynthetic Nitrogen Metabolism, Nitrogen Absorption, and Nitrogen Utilization of Hydroponic Rice

Phosphorus (P) and potassium (K) stress significantly affect the growth, physiological characteristics, and nutrient uptake of rice plants. This study investigated the photosynthetic nitrogen (N) metabolism, N uptake, and N utilization of plants under varied P and K supplies. Two local conventional high-yield rice varieties (Shennong 265 and Liaojing 294) were used. These varieties were subjected to the following hydroponic experimental treatments: HPHK (normal P and K concentrations), HPLK (normal P and 1/20 normal K concentration), LPHK (normal K and 1/20 normal P concentration), and LPLK (1/20 normal P and K concentrations). The results showed that the mesophyll cells had a relatively complete nuclear and chloroplast structures, and the antioxidant enzymes of the plants were significantly reduced under the HPHK treatment. Compared to the LP treatments (LPHK and LPLK), the HPHK treatment was found to have the following potential effects: effectively optimize plant configuration; promote leaf development (Pn, E, Ci, and Tr, chlorophyll, and leaf area index); significantly increase the N-metabolism-related enzyme activity of leaves and roots and the accumulation of N in the plant in the main growth stages; and significantly increase the rice yield and N-related efficiency. In conclusion, the HPHK treatment was found to be beneficial in improving the plant configuration, promoting photosynthetic N metabolism, and increasing grain yield and N-related utilization efficiency.

Projecting Climate Change Impact on Precipitation Patterns during Different Growth Stages of Rainfed Wheat Crop in the Pothwar Plateau, Pakistan

In rainfed areas, precipitation variations directly impact wheat growth stages such as emergence, tillering, jointing and booting, and maturity. Evaluating the impact of climate change on precipitation patterns during these critical growth stages is crucial for adapting climate change and ensuring global food security. In this study, projections of five General Circulation models (GCMs) under two shared socioeconomic pathways (SSPs) were used to predict the changing characteristics of precipitation during four main growth stages of wheat in the rainfed region of the Pothwar Plateau, Pakistan. Historical datasets of daily precipitation at six weather stations were analyzed to check the past changes in the precipitation patterns. During the baseline period (1985–2014), the annual average precipitation decreased at a rate of −9.75 mm/decade, while the amount of precipitation during the rabi season (wheat-growing season) decreased at a rate of −20.47 mm/decade. An increase in the precipitation was found during the fourth (flowering) stage of crop growth, while the first three stages experienced a decrease in the precipitation amount. The multimodal ensembled data, under the SSP2-4.5 scenario, revealed a significant decline (at the rate of −16.63 mm/decade) in the future annual precipitation. However, it is projected that, under SSP2-4.5, there may be a slight increase (4.03 mm/decade) in the total precipitation amount during the future rabi season. Under the SSP5-8.5 scenario, average annual precipitation exhibited a slightly increasing trend, increasing by 1.0 mm/decade. However, during the rabi season, there was a possibility of a decrease in precipitation amount, with a rate of 11.64 mm/decade. It is also expected that the precipitation amount may vary significantly during the crown root initiation, jointing and booting, and flowering stages in the near future. These results provide a framework for the planning of wheat production in the Pothwar region of Pakistan, taking into account the potential impact of shifting weather patterns, particularly in terms of uneven precipitation.

Distribution, characterization and chemical management of noxious shrub weed (Dichapetalum stuhlmannii Engl) in cashew in southeastern Tanzania

Weeds are among the limiting factors that influence low production of economically important crops including cashew (Anacardium occidental L.). Shrub weeds attribute to inter-competition for resources, hinder harvesting and ultimately reduce cashew yields in Tanzania. The current study determined the distribution, characteristics and chemical control option of Dichapetalum Engl in Lindi and Mtwara regions, Southeastern Tanzania. The distribution study involved a survey for weed presence along the areas of Lindi and Mtwara regions. The characterization included distilling, and assessing the growth and development properties of shrub weed. Chemical herbicides; glyphosate 480g. ai/l, 2, 4 D - Dichlorirophenoxyacetic 720g. ai/l, triclopyr 160g a.i./l and 1:1 mixture of glyphosate 480g a.i./l plus triclopyr 160g a.i./L at 15, 20 and 25 mls of formulated product/L of water per 4 m2 were tested on tender, mature and blooming growth stages of D. Stuhlmannii and three spraying frequencies. Findings revealed that the D.stuhlmannii, is a widely distributed shrub weed in Lindi and Mtwara regions. The weed was characterized with three main growth stages of seedling, mature and blooming with a long tap root and evergreen throughout the wet and dry seasons. The tested herbicides revealed the potential suppression of D. Stuhlmannii growth. Glyphosate and a mixture of glyphosate + triclopyr at 15 ml/L outperformed triclopyr and 2, 4 D across all the growth stages. Double spraying of glyphosate and its mixture bettered frequencies of triclopyr and 2, 4 D. The delayed regrowth of suppressed shrub weed took 90–120 days after application of herbicides. The current study recommends for single or double applications of glyphosate herbicides at 15 ml/L or 10,700 ml/ha on tender or mature D. Stuhlmannii in cashew farms. Further studies on the economic feasibility and effect on the microbiota of applied fungicides are required.

Integrating Unmanned Aerial Vehicle-Derived Vegetation and Texture Indices for the Estimation of Leaf Nitrogen Concentration in Drip-Irrigated Cotton under Reduced Nitrogen Treatment and Different Plant Densities

The accurate assessment of nitrogen (N) status is important for N management and yield improvement. The N status in plants is affected by plant densities and N application rates, while the methods for assessing the N status in drip-irrigated cotton under reduced nitrogen treatment and different plant densities are lacking. Therefore, this study was conducted with four different N treatments (195.5, 299, 402.5, and 506 kg N ha−1) and three sowing densities (6.9 × 104, 13.8 × 104, and 24 × 104 plants ha−1) by using a low-cost Unmanned Aerial Vehicle (UAV) system to acquire RGB imagery at a 10 m flight altitude at cotton main growth stages. We evaluated the performance of different ground resolutions (1.3, 2.6, 5.2, 10.4, 20.8, 41.6, 83.2, and 166.4 cm) for image textures, vegetation indices (VIs), and their combination for leaf N concentration (LNC) estimation using four regression methods (stepwise multiple linear regression, SMLR; support vector regression, SVR; extreme learning machine, ELM; random forest, RF). The results showed that combining VIs (ExGR, GRVI, GBRI, GRRI, MGRVI, RGBVI) and textures (VAR, HOM, CON, DIS) yielded higher estimation accuracy than using either alone. Specifically, the RF regression models had a higher accuracy and stability than SMLR and the other two machine learning algorithms. The best accuracy (R2 = 0.87, RMSE = 3.14 g kg−1, rRMSE = 7.00%) was obtained when RF was applied in combination with VIs and texture. Thus, the combination of VIs and textures from UAV images using RF could improve the estimation accuracy of drip-irrigated cotton LNC and may have a potential contribution in the rapid and non-destructive nutrition monitoring and diagnosis of other crops or other growth parameters.

Phytohormone biosynthesis and transcriptional analyses provide insight into the main growth stage of male and female cones Pinus koraiensis.

The cone is a crucial component of the whole life cycle of gymnosperm and an organ for sexual reproduction of gymnosperms. In Pinus koraiensis, the quantity and development process of male and female cones directly influence seed production, which in turn influences the tree's economic value. There are, however, due to the lack of genetic information and genomic data, the morphological development and molecular mechanism of female and male cones of P. koraiensis have not been analyzed. Long-term phenological observations were used in this study to document the main process of the growth of both male and female cones. Transcriptome sequencing and endogenous hormone levels at three critical developmental stages were then analyzed to identify the regulatory networks that control these stages of cones development. The most significant plant hormones influencing male and female cones growth were discovered to be gibberellin and brassinosteroids, according to measurements of endogenous hormone content. Additionally, transcriptome sequencing allowed the identification of 71,097 and 31,195 DEGs in male and female cones. The synthesis and control of plant hormones during cones growth were discovered via enrichment analysis of key enrichment pathways. FT and other flowering-related genes were discovered in the coexpression network of flower growth development, which contributed to the growth development of male and female cones of P. koraiensis. The findings of this work offer a cutting-edge foundation for understanding reproductive biology and the molecular mechanisms that control the growth development of male and female cones in P. koraiensis.

Estimating Maize Maturity by Using UAV Multi-Spectral Images Combined with a CCC-Based Model

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R2 value was 0.704, the RMSE was 34.58 μg/cm2, and the MAE was 26.27 μg/cm2. The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R2 values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale.

Estimation of leaf nitrogen content in winter wheat based on continuum removal and discrete wavelet transform

ABSTRACT Accurate and rapid estimation of leaf nitrogen content (LNC) in winter wheat using hyperspectral techniques is important for growth monitoring and accurate fertilization. Based on field experiments conducted over four years at three ecological sites with four different nitrogen (N) application treatments and four different N-efficient wheat varieties in Henan Province, the canopy spectra and LNC of winter wheat were obtained simultaneously in the main growth stages. The original spectrum was subjected to continuum removal (CR), the correlation between LNC and various vegetation indices was systematically analysed, and the optimal vegetation index estimation model for LNC was constructed. Simultaneously, discrete wavelet transform (DWT) was used to further compress and extract the CR spectrum. The model was combined with partial least squares regression (PLSR) and K-nearest neighbour (KNN) algorithms to estimate LNC in winter wheat. The results showed that the spectrum treated with CR was significantly improved in its correlation with LNC and that the model established based on normalized vegetation index NDVI (CR728, CR977) combined with the CR spectrum was better compared to existing vegetation indices. The calibration and validation coefficients of determination (R2) and root mean square error (RMSE) were 0.816 and 0.799, and 0.352% and 0.342%, respectively. DWT was used to transform the CR spectrum, and the results showed that a combination of the CR spectrum and a sym8 wavelet function with PLSR based on the approximation coefficients at decomposition level 2 predicted LNC most accurately. The coefficient of determination RC 2, root mean square error RMSEC, and relative percent deviation (RPDC) of the calibration set were 0.884, 0.279%, and 2.932, respectively, and RV 2, RMSEV, and RPDV of the validation set were 0.855, 0.291%, and 2.619, respectively, indicating that the model had good stability and predictive ability. Combination of CR and DWT improved the modelling accuracy of wheat LNC and showed better prediction results for multi-year and multi-point samples. The results of the study can provide a basis and reference for rapid monitoring of LNC in winter wheat.

Effect of different NPK fertilization timing sequences management on soil-petiole system nutrient uptake and fertilizer utilization efficiency of drip irrigation cotton

In order to elucidate the effects of different nitrogen (N), phosphorus (P), and potassium (K) fertilization timing sequences management on nutrient absorption and utilization in drip irrigation cotton, field experiments were conducted from 2020 to 2021. There are six timing sequences management methods for NPK fertilization (S1–S6: 1/3Time N–1/3Time PK–1/3Time W, 1/3Time PK–1/3Time N–1/3Time W, 1/2Time NPK–1/2Time W, 1/4Time W–1/4Time N–1/4Time PK–1/4Time W, 1/3Time W–1/3Time NPK–1/3Time W), among which S6 is the current management method for field fertilization timing sequences, and S7 is the non N. The results showed that during the main growth stage, S5 accumulated more nitrate nitrogen (NO3–-N) and ammonium nitrogen (NH4+-N) content in soil between 20 and 40 cm, and accumulated more available phosphorus content in soil between 5–15 cm and 15–25 cm, S5 reducing N leaching and increasing P mobility. It is recommended to change the timing sequences management method of NPK fertilization for drip irrigation cotton to 1/4Time W–1/4Time PK–1/4Time N–1/4Time W, which is beneficial for plant nutrient absorption and utilization while reducing environmental pollution.

Probabilistic assessment of drought impacts on wheat yield in south-eastern Australia

A risk-based approach is more meaningful to quantify the effects of drought on crop yield given the randomness nature of past drought events, compared to the deterministic approach. However, the majority of these probabilistic studies are conducted at national or global scale to assess the yield loss probability under given drought conditions. There is still a lack of research combining droughts and crop yields in a probabilistic way at a local scale. Moreover, it is unclear how drought threshold triggering yield loss at a given conditional probability will vary in dryland cropping regions. Here, we used wheat yield data from 66 shires in New South Wales (NSW) wheat belt and meteorological data from 986 weather stations. A copula-based probabilistic method was developed to explore the yield loss probability to various drought conditions. We investigated the drought threshold under a given yield loss probability using the constructed copula function. We found that SPEI-6 in October was the optimal drought index to represent detrended wheat yield variation as this period covered the main growth stages of winter wheat in the study region. Our results show that as the severity of drought increased, the wheat yield loss probability also increased. Yield loss probability varied among the study shires, mainly due to the various climate conditions of each region. The drought threshold in subregion 1 (the northwest) was highest, followed by subregion 2 (the southwest) and subregion 3 (the eastern), indicating that wheat yield in subregion 1 was more sensitive to drought. The findings could provide important direction and benchmarks for stakeholders in evaluating the agricultural impact of drought, especially in those drought prone areas. We expect that the methodological framework developed here can be extended to other dryland areas to provide helpful information to growers, risk management policy makers and agricultural insurance evaluators.

Heat stress decreased transpiration but increased evapotranspiration in gerbera.

Heat stress is a major constraint for plant production, and evapotranspiration is highly linked to plant production. However, the response mechanism of evapotranspiration to heat stress remains unclear. Here, we investigated the effects of heat stress during two main growth stages on transpiration and evapotranspiration of gerbera. Two levels of day/night temperature were adopted during the vegetative growth stage (VG) and the flowering bud differentiation stage (FBD), namely control (CK; 28/18 °C) and heat stress (HS; 38/28°C) levels. The duration of HS was set as 5, 10, 15, and 20 days, respectively. At the beginning of HS, hourly transpiration was mainly inhibited near noon. With continuation of HS, the duration and extent of inhibition of hourly transpiration increased. Daily transpiration rate was also markedly reduced by HS during the VG (18.9%-31.8%) and FBD (12.1%-20.3%) stages compared to CK. The decrease in the daily transpiration rate was greater for longer duration of heat stress. This reduction of transpiration was the main contributor to stomatal limitation at the beginning of HS, while additional inhibition of root activity, leaf area, and root biomass occurred under long-term HS. The daily transpiration rate could not recover after the end of HS (so-called recovery phase), except when HS lasted 5 days during the VG stage. Interestingly, daily evapotranspiration during HS was substantially increased during the VG (12.6%-24.5%) and FBD (8.4%-17.6%) stages as a result of more increased evaporation (100%-115%) than reduced transpiration. However, during the recovery phase, the daily evapotranspiration was markedly decreased at the VG (11.2%-22.7%) and FBD (11.1%-19.2%) stages. Hence, we suggest that disproportionate variation of transpiration and evaporation during HS, especially at the recovery phase, should be considered in various evapotranspiration models and climate scenarios projections.

The metabasites from the Texel Unit (Austroalpine nappe stack): markers of Cretaceous intracontinental subduction and subsequent collision

Abstract Eclogites in the Texel Unit (Austroalpine nappe stack; Eastern Alps, South Tyrol, Italy) represent the westernmost out-crops of the E-W striking Eoalpine High-Pressure Belt (EHB). East of the Tauern Window, the EHB forms part of a Cretaceous intracontinental south-dipping subduction/collision zone. The impetus of this contribution is to extend the geothermo-barometric data of the eclogites from the EHB using conventional geothermobarometry and multi-equilibrium calculations as well as Zr-in-rutile/titanite geothermometry and to put the P-T results of eclogites and amphibolites from the Texel Unit into the geodynamic framework. The investigated samples of this study are from the Spronser- and the Saltaus valleys (S-Tyrol) in the Austroalpine Texel Unit. The Texel Unit is composed mainly of paragneisses with minor intercalations of micaschists, orthogneisses, amphibolites and subordinately eclogites. The amphibolites from the Spronser valley contain the mineral assemblage amphibole + plagioclase + garnet + clinozoisite/epidote + quartz + titanite ± ilmenite ± rutile ± apatite ± calcite. Chemical zoning in plagioclase and amphibole shows two main growth stages: an older P-dominated stage (e.g. albite and barroisite cores) and a younger amphibolite-facies stage. The core of the amphiboles shows barroisite composition, the rim can be chemically classified as hornblende, edenite, tschermakite and pargasite. Geothermobarometric calculations with multi-equilibrium geothermobarometry (THERMOCALC v.3.21) yield temperatures of 600–654°C and pressures of 0.98–1.17 GPa for the same samples. The eclogites from the Saltaus valley contain the peak mineral assemblage omphacite + amphibole + garnet + clinozoisite/epidote + muscovite + quartz + titanite ± ilmenite ± rutile. Thermobarometric calculations, were performed in the system CaO-FeO-MgO-Na2O-Al2O3-FeO-SiO2-H2O with the assemblage clinopyroxene + garnet + amphibole + clinozoisite/epidote + muscovite + quartz ± H2O. The calculations involved an H2O-absent invariant point (mode-1 calculation) as well as two types of average P-T mode-2 calculations. The obtained average P-T H2O-absent mode 1 P-T conditions are 1.89 ± 0.18 GPa and 578 ± 60°C. Using the average P-T mode-2 two types of calculations were done: (1) calculations without amphiboles but with H2O present, which yield mean P-T conditions of 1.95 ± 0.28 GPa and 601 ± 55°C and (2) calculations with amphiboles but without H2O, which yield mean P-T conditions of 1.95 ± 0.26 GPa and 666 ± 77°C. Calculations using THERMOCALC v.3.33 yield similar results with slightly higher pressures of 0.3 GPa. Based on the present geothermobarometric data in conjunction with available mineral ages the eclogites represent the Eoalpine intracontinental subduction stage whereas the amphibolites reflect the subsequent, P-accentuated stage of decompression associated with the subsequent Eoalpine collisional stage.

Special Microbial Communities Enhanced the Role of Aged Biochar in Reducing Cd Accumulation in Rice

Biochar exhibits a good adsorption ability for heavy metals in soil and has been widely used as a remediation material in Cd-contaminated soil. However, the status of Cd uptake by rice driven by soil physicochemical properties and rhizosphere microbial communities after years of biochar application is not well understood. In this study, the relationship between the rhizosphere microbial community and soil physicochemical properties and rice Cd accumulation were investigated during the main rice growth stages. The results showed that in comparison to the non-biochar treatment (control), a noticeable reduction in Cd content in rice stem sheaths, leaves, rice husks and milled rice with different growth stages were observed in the biochar treatment after four years, which decreased by 38.76–66.18%, 40.93–70.27%, 43.64–47.92% and 31.91–34.38%, respectively. Compared to non-biochar treatment (control), the properties of the soil in different growth stages by biochar treatment of the soil pH, soil organic matter (SOM), total nitrogen (TN) and available phosphorus (AP) were significantly increased, which increased by 10.5–16.13%, 8–25%, 75–130.13% and 132.95–191.43%, respectively. The content of available Cd (ACd) concentration in different stages by biochar treatment was significantly decreased, which decreased by 26.57–44.24%. Biochar application after four years changed the rhizosphere bacterial community structure composition (phyla level) in all stages. The relative abundance of Proteobacteria, Bacteroidetes and Nitrospirae was increased, while the relative abundance of Chloroflexi, Acidobacteria and Actinobacteria was decreased. Meanwhile, the biochar application enriched Rhodocyclaceae, Burkholderiaceae, Nitrosomonadaceae, Anaerolineaceae, Ignavibacteriales and Bacteroidales, which may contribute to the reduction of Cd uptake and accumulation in rice. These results suggest that biochar treatment after four years changed the rhizosphere microbial community structure and soil physicochemical properties and promoted the colonization of specific microbial populations in the rice rhizosphere to form a special protective system in the rice rhizosphere, which reduced Cd uptake by rice.

An improved model to simulate soil water and heat: A case study for drip-irrigated tomato grown in a greenhouse

Soil physical, chemical, and biological processes are influenced by soil water and heat conditions, which greatly depend on the crop root systems in different soil layers. Hence, understanding root distribution, soil water and heat dynamics is essential to improve water and nutrient use efficiency. A case study of tomato grown in a greenhouse with drip irrigation was conducted to test an improved two-dimensional (2D) model, namely UZflow-2D, for simulating the soil water and heat dynamics. The performance of UZflow-2D model was also compared with the well-known Hydrus-2D model. A 2D root length density (RLD) function, as a main subsidiary model of UZflow-2D, was proposed by investigating the roots distribution from lateral and radial latitudes at the four main growth stages (seedling, flowering, fruit-setting, and picking). Experimental data in 2015 was used to calibrate the parameters of UZflow-2D and Hydrus-2D models, and validated using the data of 2016. Results showed that more than 70% of the total RLD was concentrated in soil layers of 0–20 cm during the seedling, 0–40 cm during the flowering, and 0–60 cm during the fruit-setting and picking stages. The 2D RLD function perform well for locations within and between two rows, with determination coefficients higher than 0.77. Both UZflow-2D and Hydrus-2D models perform well in simulating soil water and soil temperature dynamics, while UZflow-2D model produced improved accuracy in modeling the soil water and heat dynamics within and between two rows of drip-irrigated tomato plants. The overall root mean square error (RMSE) was ∼0.008 cm3 cm−3 for soil water contents, and ∼0.434 °C for soil temperature for UZflow-2D model. However, the overall RMSE was ∼0.012 cm3 cm−3 for soil water contents, and ∼0.504 °C for soil temperature for Hydrus-2D model. Hence, the UZflow-2D model can be served as an alternatively useful tool to simulate water and heat dynamics.

Retrieving soil moisture from grape growing areas using multi-feature and stacking-based ensemble learning modeling

Soil moisture (SM) is an essential parameter for crop growth and development, and temporal and spatial variation in SM in agricultural fields varies by crop type due to corresponding crop growing characteristics and cultivating patterns. Few studies have performed SM retrieval in grape growing areas, and SM estimation using only spectral reflectance (SR) or derived drought indices may not be wholly accurate. In this study, seven features based on evapotranspiration (ET), land surface temperature (LST), and SR were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data (8-day temporal resolution and 500 m spatial resolution) and integrated with topography feature to determine SM during the main growth stages of grape in the eastern foothills of the Helan Mountains from 2009 to 2018. A total of 25-period models covering April–October (8-day temporal resolution) were constructed. Finally, the models were achieved to retrieve SM, and the spatiotemporal distribution pattern was analyzed. The stacking ensemble algorithm can integrate multiple models for better retrieval accuracy and overcome the limitations of individual machine learning models. We also compared the SM estimation accuracy of three single machine learning models (Category boosting, random forest, and gradient boosting decision tree) with the ensemble model using the stacking algorithm. The results indicated that the stacking-based ensemble model could retrieve SM more accurately and stably than any individual machine learning algorithm, and the stacking-based ensemble model showed good applicability during the main growth stages of grape. The average coefficient of determination (R2) and root mean square error (RMSE) of the 25-period multi-feature stacking-based models were 0.7504 and 0.0245 m3/m3. Overall, the application results showed that spring and summer droughts were more severe than autumn droughts in the study area during the main growth stages of grape. Our findings indicate that using multiple features and the stacking-based ensemble model could improve SM estimation accuracy in grape growing areas.

Effects of Mechanical Transplanting Methods and Planting Geometry on Yield Formation and Canopy Structure of Indica Rice under Rice-Crayfish Rotation

The rice–crayfish continuous production system developed rapidly due to its high economic benefits and eco-friendly nature in China. This study explored the effects of mechanically transplanted methods and planting geometry on the relationship between rice yield and canopy structure, under rice-crayfish rotation using excellent-quality indica rice, and carried out in 2018 and 2019. Three mechanical transplantation methods were set as follows: carpet seedlings mechanically transplanted with 30 cm equal row spacing (CMTE), pot seedlings mechanically transplanted with narrow row spacing with alternating 23 cm/33 cm wide row spacing (PMTWN), and equal row spacing at 28 cm (PMTE). Different plant spacings (CMTE1-CMTE6, PMTWN1-PMTWN6, PMTE3, and PMTE4) were set in accordance with different mechanical transplanting methods. CMTE and PMTWN both included six transplanting densities, while PMTE included 2 transplanting densities. Results showed that rice yield was improved by 2.87–6.59% under PMTWN when compared to CMTE, which was mainly due to the increase in spikelets per panicle and filled-grain percentage. Dry matter accumulation was increased and larger leaf area indexes were observed under PMTWN than CMTE at the rice main growth stage. Yield of CMTE and PMTWN treatments increased at first and then declined with decreased planting density. Under suitable planting density, PMTWN could optimize rice population structure and increase rice yield compared with PMTE. For tested rice variety, pot seedlings were mechanically transplanted alternating alternating 23 cm/33 cm wide row spacing, combined with a plant spacing of 16.8 cm, was proper for its yield improvement under rice-crayfish rotation.

Response of Grain Yield and Water Use Efficiency to Irrigation Regimes during Mid-Season indica Rice Genotype Improvement

Understanding the performance of rice (Oryza sativa L.) agronomic traits and efficiency in water usage as well as grain yield under various irrigation regimes is crucial to achieving high resource use efficiency and high yield. In this study, 12 mid-season indica rice genotypes that have been grown in the lower reaches of the Yangtze River for the past 80 years were studied in a field experiment for two years under two irrigation regimes, i.e., conventional irrigation (CI) and alternate wetting and drying irrigation (AWD). The results showed that with genotype improvement in irrigation regimes, the total number of spikelets, shoot and root dry weight, root oxidation activity, total leaf area index (LAI), effective LAI, leaf photosynthetic rate, and abscisic acid contents and zeatin + zeatin riboside contents in root bleeding sap were significantly increased at main growth stages. AWD irrigation synchronously increased rice resource use efficiency (water use efficiency (WUE), radiation use efficiency (RUE), and temperature use efficiency (TUE)) and grain yield. Compared to CI, AWD more significantly enhanced the performances of rice genotypes in all studied traits. Based on our findings, a semi-dwarf hybrid rice genotype has great potential for high resource use efficiency and high yield under alternate wetting and drying irrigation, which was attributed to the improved agronomic characteristics and superior root traits.

Impact of Transgenic Cry1Ab/2Aj Maize on Abundance of Non-Target Arthropods in the Field.

Transgenic Bacillus thuringiensis (Bt) maize has broad prospects for application in China. Before commercialization, it is necessary to assess possible ecological impacts, including impacts on non-target arthropods (NTAs) in the field. In the present study, transgenic Bt maize expressing cry1Ab/2Aj and its corresponding non-transformed near isoline were planted under the same environmental and agricultural conditions, and arthropods in the field were collected during the three main growth stages of maize. In a one year trial, the results showed the composition of NTA communities in the transgenic and control maize fields were similar. There were no significant differences for community-level parameters of species richness (S), Shannon–Wiener diversity index (H′), evenness index (J) and Simpson’s dominant concentration (C) between the two types of maize fields. Likewise, a Bray–Curtis dissimilarity and distance analysis showed that Cry1Ab/2Aj toxin exposure did not increase community dissimilarities between Bt and non-Bt maize plots and that the structure of the NTAs community was similar on the two maize varieties. Furthermore, planting of the transgenic cry1Ab/2Aj maize did not affect the density or composition of non-target decomposers, herbivores, predators, parasitoids and pollinator guilds. In summary, our results showed that planting of Bt maize producing Cry1Ab/Cry2Aj proteins do not adversely affect population dynamics and diversity of NTAs.

Distribution, behavior, and risk assessment of chlorinated paraffins in paddy plants throughout whole growth cycle.

Paddy plants provide staple food for 3 billion people worldwide. This study explores the environmental fate and behavior of a high-volume production emerging contaminants chlorinated paraffins (CPs) in the paddy ecosystem. Very-short-, short-, medium-, and long-chain CPs (vSCCPs, SCCPs, MCCPs, and LCCPs, respectively) were analyzed in specific tissue of paddy plants at four main growth stages and soils from the Yangtze River Delta, China throughout a full rice growing season. The total CP concentrations in the paddy roots, stalks, leaves, panicles, hulls, rice, and soils ranged from 181 to 1.74×103, 21.7-383, 19.6-585, 108-332, 245-470, 59.6-130, and 99.6-400ng/g dry weight, respectively. The distribution profile indicated the translocation of SCCPs and MCCPs from soils to paddy tissue, highlighting their elevated bioaccumulative potential. The evolution of CP level/mass/pattern during the whole growth cycle suggested atmospheric CPs deposition on leaves and hulls, as well as stalk-rice transfer. CSOIL plant uptake model well predicted the level, distribution pattern, and bioconcentration factors (BCFs) of SCCPs and MCCPs in paddy shoot and recognized the soil-air-shoot pathway as the major contributor. Moreover, risk evaluation indicated that MCCPs intake and subsequent risks dominated the total exposure to CPs via rice ingestion. This is the first report on the occurrence, fate and risk assessment of all CPs classes in paddy ecosystems, and the results underline the potential health effects caused by the in-use MCCPs via rice ingestion.

Integrating an attention-based deep learning framework and the SAFY-V model for winter wheat yield estimation using time series SAR and optical data

Information on the spatial distribution of yields can be obtained over a large area by using remote sensing (RS) data. Combining Synthetic Aperture Radar (SAR), being sensitive to above ground biomass and soil moisture in all weather conditions, and optical data can improve the usability of RS data and provide a basis for pixel-based crop yield estimation (YE). In this study, an Upscaled Convolutional Gated Recurrent Unit model incorporated an attention mechanism (UpSc-AConvGRU model) was proposed to improve the estimation accuracy of the winter wheat growth parameter, Leaf Area Index (LAI). Gap filling the time series of optical data was done with backscatter coefficients, local incidence angles and polarimetric decomposition information from Sentinel-1 SAR imagery. The time series LAI estimated by the UpSc-AConvGRU model and Vegetation Temperature Condition Index (VTCI) retrieved from Sentinel-3 optical imagery were then used as state variables of the SAFY-V model to estimate winter wheat yield. The results showed that the proposed UpSc-AConvGRU model incorporated the Convolutional Block Attention Module (CBAM) can effectively improve the accuracy of LAI estimation, with RMSEs ranging from 0.413 to 0.699 m2 m2 for LAI estimated within main growth stages (MGSs) of winter wheat. The correlation between estimated LAI and Sentinel-3 retrieved LAI was generally higher at irrigated farmland compared to rain-fed farmland. The estimated LAI was closest to Sentinel-3 retrieved LAI at the green-up and late heading-filling stage of winter wheat, followed by the jointing and early heading-filling stage, and finally the milk maturity stage. There was good agreement between the SAFY-V model estimated and field measured winter wheat yields (R2 = 0.546, RMSE = 0.757 t ha−1), and the estimated yields at the pixel scale in the Guanzhong Plain, PR China were satisfactory. This study combined deep learning and crop growth modeling, proposed a new pixel scale winter wheat YE method.