Post-heading Stage Research Articles

Optimizing wheat supplementary irrigation: Integrating soil stress and crop water stress index for smart scheduling

A two-year field experiment was conducted to integrate soil moisture stress with the Crop Water Stress Index (CWSI) for optimizing irrigation in winter wheat (Triticum aestivum L.) under varying irrigation regimes. The study took place at the Water Technology Centre (WTC-02) of ICAR-IARI, New Delhi, where the climate shows a blend of monsoon-influenced humid subtropical and semi-arid conditions. Using a randomized block design (RBD), five irrigation treatments were applied: full irrigation and deficit irrigation (DI) at 15 %, 30 %, 45 %, and 60 % levels. Canopy and ambient air temperature data, along with vapor pressure deficit (VPD), were recorded using a developed integrated sensing device to empirically determine the lower baseline equations and upper threshold for CWSI computation at pre-heading and post-heading stages. The slope (m), intercept (c) of the lower baseline equation, and upper threshold (UL) for pre-heading and post-heading were found: m: −1.94, c: −1.33, UL: 1.92°C and m: −1.30, c: −2.37, UL: 2.0°C, respectively. Results showed that increasing water deficit levels led to significant reductions in grain yield, biomass production, and harvest index. A strong negative correlation (R² = 0.95 and 0.93) between mean seasonal CWSI and yield attributes highlighted the utility of CWSI in yield prediction under varying irrigation regimes. It is recommended to schedule irrigation based on the CWSI approach when CWSI ≥0.35 for optimum wheat yields. Integrating CWSI with soil moisture stress provides valuable real-time insights into crop water status, enabling more precise and smart irrigation scheduling.

Estimating wheat spike-leaf composite indicator (SLI) dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to grain yield (GY) has been overlooked in the accurate spectral prediction of yield. Thus, it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis. Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years, our objectives were to (i) construct a yield-related phenotypic trait (spike–leaf composite indicator, SLI) accounting for the contribution of the spike to photosynthesis, (ii) develop a novel spectral index (enhanced triangle vegetation index, ETVI3) sensitive to SLI, and (iii) establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms. The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index. ETVI3 maintained a strong correlation with SLI throughout the growth stage, whereas the correlations of other spectral indices with SLI were poor after spike emergence. Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI, with the most accurate estimates of SLI showing coefficient of determination, root mean square error (RMSE), and relative RMSE values of 0.71, 0.047, and 26.93%, respectively. These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY. This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Transcription factor OsSHR2 regulates rice architecture and yield per plant in response to nitrogen.

Mutation of OsSHR2 adversely impacted root and shoot growth and impaired plant response to N conditions, further reducing the yield per plant. Nitrogen (N) is a crucial factor that regulates the plant architecture. There is still a lack of research on it. In our study, it was observed that the knockout of the SHORTROOT 2 (OsSHR2) which was induced by N deficiency, can significantly affect the regulation of plant architecture response to N in rice. Under N deficiency, the mutation of OsSHR2 significantly reduced root growth, and impaired the sensitivity of the root meristem length to N deficiency. The mutants were found to have approximately a 15% reduction in plant height compared to wild type. But mutants showed a significant increase in tillering at post-heading stage, approximately 26% more than the wild type, particularly in high N conditions. In addition, due to reduced seed setting rate and 1000-grain weight, mutant yield was significantly decreased by approximately 33% under low N fertilizer supply. The mutation also changed the distribution of N between the vegetative and reproductive organs. Our findings suggest that the transcription factor OsSHR2 plays a regulatory role in the response of plant architecture and yield per plant to N in rice.

A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing

Diagnosing water conditions timely and accurately is crucial for seasonal irrigation scheduling in crop production. The purpose of this study was to establish robust water deficit models of winter wheat in different growing seasons by combining unmanned aerial vehicle (UAV) multispectral and thermal images. In 2021 and 2022, a water deficit field experiment on winter wheat was carried out in Hebei Province, China. Five-band multispectral and thermal images were obtained with a UAV at six key growth stages of winter wheat. Fourteen vegetation indices (VIs) and two thermal indices (TIs) were calculated. Simultaneously, wheat stomatal conductance and soil water content were measured. On this basis, normalized stomatal conductance (NGS) and effective water content (EWC) were calculated. TIs had the highest correlation with NGS and EWC at early growth stages, whereas ratio vegetation index (RVI), modified simple ratio index (MSR) and normalized difference vegetation index (NDVI) were highly correlated at later stages. Partial least squares (PLS), support vector machine (SVM) and gradient boosting decision tree (GBDT) were used to predict NGS and EWC for each growth stage, with the data of 2021 as the training set and the data of 2022 as independent test set. In general, GBDT outperformed PLS and SVM, and NGS was better predicted than EWC. Including VIs and TIs effectively improved the estimation accuracy of the predictive models. The test set results of the NGS and EWC models built by GBDT achieved the best performance in flowering stage (coefficient of determination (R2) = 0.88, root mean square error (RMSE) = 0.08, normalized root mean square error (NRMSE) = 14.7%) and filling stage (R2 = 0.90, RMSE = 0.05, NRMSE = 15.9%), respectively. The models of the post-heading stage were better than those of the pre-heading stage for both NGS and EWC. This study provides a robust method for diagnosing water stress only using UAV remote sensing data.

Delayed Leaf Senescence Improves Radiation Use Efficiency and Explains Yield Advantage of Large Panicle-Type Hybrid Rice

Super hybrid rice with predominantly large panicle types has achieved remarkable success in enhancing crop yield. However, when compared with multi-panicle-type varieties, the yield stability of large panicle-type varieties remains a challenge, and limited information is available on the comparative advantages of multi-panicle types. Consequently, a two-year experiment was conducted to evaluate the grain yield, biomass production, leaf area index (LAI), and radiation use efficiency (RUE) of large panicle-type hybrid rice (Y-liangyou 900, YLY900) and multi-panicle-type hybrid rice (C-liangyouhuazhan, CLYHZ) under three nitrogen (N) treatments (0, 180, 270 kg N ha−1). The effects of increased N fertilization were more pronounced in the large panicle-type varieties. YLY900 outperformed CLYHZ in terms of average yield (6% higher), and its yield advantage was attributed to higher spikelets per panicle (28%). Due to YLY900’s RUE being 9% higher than CLYHZ, it results in a 12% greater accumulation of dry matter than CLYHZ. Furthermore, YLY900 exhibited significant improvements of 16%, 4%, and 14% in specific leaf weight, effective leaf area ratio, and LAI at 20 days after the heading stage (20DAH), respectively, compared with CLYHZ. YLY900 also demonstrated a stronger correlation between rice yield and intercepted photosynthetically active radiation (IPAR) compared with CLYHZ, with R2 values of 0.80 and 0.66, respectively. These findings highlight the superior performance of YLY900, resulting from higher light interception percentage (IP) and IPAR values, which consequently led to enhanced RUE and grain yield. Our research reveals that delayed leaf senescence by increasing LAI at the post-heading stage for large panicle-type hybrid rice, thereby contributing to greater RUE, led to higher biomass production and grain yield.

Establishment of Crop Water Stress Index for Sustainable Wheat Production under Climate Change in a Semi-Arid Region of Pakistan

The Crop Water Stress Index (CWSI) is a useful tool for evaluating irrigation scheduling and achieving water conservation and crop yield goals. This study examined the CWSI under different water stress conditions for the scheduling of wheat crop irrigation and developed indices using the leaf canopy temperature in Faisalabad, Pakistan. The experiments were conducted using a randomized, complete block design and four irrigation treatments with deficit levels of D0%, D20%, and D40% from the field capacity (FC) and D100% (100% deficit level). The CWSI was determined at pre-heading and post-heading stages through the lower baseline (fully watered crop) and upper limit (maximum stress). These baselines were computed using the air temperature and canopy temperature of plant leaves and the vapor pressure deficit (VPD). The CWSI for each irrigation treatment was calculated and the average seasonal CWSI value for the whole season was used to develop the empirical relationships for scheduling irrigation. The relationships between the air canopy temperatures and the VPD resulted in slope (x) = −0.735 and interception (c) = −0.8731 as well as x = −0.5143 and c = −1.273 at the pre- and post-heading stages, respectively. The values of the CWSI for the treatment at deficit levels of D0%, D20%, D40%, and D100% were found to be 0.08, 0.61, 0.20, and 0.64, respectively. The CWSI values developed in this study can be effectively used to promote better the monitoring of irrigated wheat crops in the region.

Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

BackgroundRice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of Leaf Area Index (LAI) provides important information to evaluate rice growth and production.MethodsThis study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. Forty eight different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling.ResultsThe results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages.ConclusionsThe progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale.

Responses of Grain Yield and Yield Related Parameters to Post-Heading Low-Temperature Stress in Japonica Rice.

There is unprecedented increase in low-temperature stress (LTS) during post-heading stages in rice as a consequence of the recent climate changes. Quantifying the effect of LTS on yields is key to unraveling the impact of climatic changes on crop production, and therefore developing corresponding mitigation strategies. The present research was conducted to analyze and quantify the effect of post-heading LTS on rice yields as well as yield and grain filling related parameters. A two-year experiment was conducted during rice growing season of 2018 and 2019 using two Japonica cultivars (Huaidao 5 and Nanjing 46) with different low-temperature sensitivities, at four daily minimum/maximum temperature regimes of 21/27 °C (T1), 17/23 °C (T2), 13/19 °C (T3) and 9/15 °C (T4). These temperature treatments were performed for 3 (D1), 6 (D2) or 9 days (D3), at both flowering and grain filling stages. We found LTS for 3 days had no significant effect on grain yield, even when the daily mean temperature was as low as 12 °C. However, LTS of between 6 and 9 days at flowering but not at filling stage significantly reduced grain yield of both cultivars. Comparatively, Huaidao 5 was more cold tolerant than Nanjing 46. LTS at flowering and grain filling stages significantly reduced both maximum and mean grain filling rates. Moreover, LTS prolonged the grain filling duration of both cultivars. Additionally, there was a strong correlation between yield loss and spikelet fertility, spikelet weight at maturity, grain filling duration as well as mean and maximum grain filling rates under post-heading LTS (p < 0.001). Moreover, the effect of post-heading LTS on rice yield can be well quantified by integrating the canopy temperature (CT) based accumulated cold degree days (ACDDCT) with the response surface model. The findings of this research are useful in modeling rice productivity under LTS and for predicting rice productivity under future climates.

Varietal difference in dynamics of non-structural carbohydrates in nodal segments of stem in two varieties of rice (Oryza sativa L.) at pre- and post-heading stages

ABSTRACT The non-structural carbohydrate (NSC) stored in rice stems (culm plus leaf sheaths) at pre-heading stage is one of the major carbon sources for grain filling. In this study, we conducted field experiments in 2018 and 2019 using two varieties, Momiroman (japonica-dominant) and Teqing (indica), which have different accumulation and remobilization patterns of NSC in the stems. We analyzed NSC dynamics of each of the five nodal segments of internodes and leaf sheaths at pre- and post-heading stages. The dynamics of NSC at pre- and post-heading stages differed among segments. The lower segments of internodes and leaf sheaths accumulated more NSC at pre-heading stage compared to the upper segments in both varieties. The lower internodes in Teqing were morphologically larger than in Momiroman and would be able to store more NSC. However, the upper parts of the stems required biomass for development and formation at the stage of NSC accumulation in stems. Momiroman had longer upper internodes and leaf sheaths and required more biomass than Teqing. The NSC remobilization was later in the upper than the lower internodes for both varieties, which led to the delay of NSC remobilization in the entire culm after heading. These results indicated that ideal morphology of stems, to maximize the potential contribution of stem NSC as the carbon source for grain filling, is large lower internodes and short upper internodes and leaf sheaths.

Combining texture, color, and vegetation indices from fixed-wing UAS imagery to estimate wheat growth parameters using multivariate regression methods

Precision crop management in modern agriculture requires timely and effective acquisition of crop growth information. Recently, unmanned aerial systems (UASs) have rapidly developed and are now widely used in crop remote sensing (RS). Vegetation index (VI) and color index (CI) are commonly used RS methods to monitor crops. Texture is intrinsic information of the images, which can reflect the crop canopy structure and be used for vegetation classification. The objective of this study was to explore the potential of combining VI, CI, and texture to improve the estimation accuracy of wheat growth parameters based on fixed-wing UAS imagery. Wheat field experiments were carried out at the Xinghua Experimental Station for two consecutive years of 2017–2019 on three wheat cultivars under five nitrogen fertilization rates. Two commonly used wheat growth parameters, leaf area index (LAI) and leaf dry matter (LDM), synchronized with wheat field UAS images, were obtained at key growth stages. Simple regression (SR) was used to determine quantitative relationships between RS variables (VI, CI, and texture) and LAI, LDM. The data showed that individual texture does not correlate well with wheat growth parameters, while a texture index (TI), containing two texture measurements, showed stronger correlation with LAI and LDM. With the utilization of simple regression (SR), VI (R2 > 0.65, RRMSE < 21.87%) exhibited the best accuracy in estimating LAI and LDM, followed by TI (R2 > 0.51, RRMSE < 26.28%) and CI (R2 > 0.34, RRMSE < 27.74%). Multiple linear regression (MLR) and random forest (RF) were further employed to develop LAI and LDM estimation models using different input variable sets (VIs, VIs + CIs, and VIs + CIs + TIs). Compared with SR and MLR, the RF models that combined VIs, CIs, and TIs greatly improved the estimation accuracy of LAI and LDM, and the validated R2 of the best RF models for LAI and LDM estimation reached 0.78 and 0.78 (RRMSE = 17.32% and 13.83%) in pre-heading stages, 0.81 and 0.77 (RRMSE = 17.86% and 16.08%) in post-heading stages, and 0.76 and 0.75 (RRMSE = 18.13% and 16.79%) in all stages, respectively. This study demonstrated that image textures can assist wheat monitoring to achieve higher estimation accuracy of LAI and LDM, and fixed-wing UAS is a promising platform that can provide reliable data for large-scale crop management.

Co-planted barnyardgrass reduces rice yield by inhibiting plant above- and belowground-growth during post-heading stages

Barnyardgrass (Echinochloa spp.) is the most common noxious weed in rice paddies as it inhibits rice growth and reduces grain yield. To date, little information is available on above- and belowground-growth changes in rice due to neighboring barnyardgrass. This study aimed to investigate the changes in root traits and shoot growth of rice when it is grown with different kinds of barnyardgrass. Japonica rice plants (var. Nanjing 9108) were co-cultured with two varieties of Echinochloa crusgalli (L.) Beauv. (EP, var. mitis (pursh) Petern; EH, var. zelayensis (H.B.K.) Hitchc), and E. colonum (L.) Link (EL) in the field in 2017 and 2018. Four treatments included control (i.e., weed free rice plants) and co-cultures with each of three barnyardgrasses (EP, EH, and EL). The results revealed that EP, EH, and EL treatments significantly reduced rice grain yields by 30.6%–36.2%, 42.5%–46.5%, and 10.6%–14.3%, respectively. Shoot growth including shoot dry weight, leaf photosynthetic rate, zeatin (Z) and zeatin riboside (ZR) in grains, and activities of key enzymes involved in sucrose-to-starch conversion in grains and root traits, such as length density, root dry weight, total absorbing surface area, active absorption surface area, oxidation activity, and Z + ZR contents in roots were dramatically reduced during post-heading stages of rice when grown with the three kinds of barnyardgrass. Moreover, above-mentioned rice shoot growth indices were strongly and positively correlated with root traits. These results suggested the decrease in rice shoot growth and root traits during post-heading stages contributes to the reduction in the rice yield when it grows with barnyardgrass neighbors.

Elastic sowing dates with low seeding rate for grain yield maintenance in mechanized large-scale double-cropped rice production

Elastic sowing dates (ESDs) are correlated with rice grain yield. ESD is the easiest factor for farmers to manipulate in mechanized large-scale farming. In this study, field experiments were conducted over a 2-year period to determine the effects of different sowing dates on growth duration, effective accumulated temperature, and yield attributes in two early- and late-season machine-transplanted rice cultivars. In early rice (ER), a delay in the sowing date led to decreased grain yield and shorter growth duration. In late rice (LR), delayed sowing led to significantly lower grain yield and prolonged growth duration. In LR, significantly positive correlations were detected between effective accumulated temperature in the post-heading stage and both filling ratio and yield. Reproductive redundancy increased markedly in LR, by 7.72% over a 5-day interval. We determined that the ESDs for LR were 10 days later than the control, and that of ER was recommend early sowing rather than late sowing. These findings suggest a new strategy to meet the demands of mechanized large-scale rice farming: the development of thermal sensitive high-yield long-duration ER cultivars and high-yield short-duration LR cultivars.

Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning

Non-destructive and accurate estimation of crop biomass is crucial for the quantitative diagnosis of growth status and timely prediction of grain yield. As an active remote sensing technique, terrestrial laser scanning (TLS) has become increasingly available in crop monitoring for its advantages in recording structural properties. Some researchers have attempted to use TLS data in the estimation of crop aboveground biomass, but only for part of the growing season. Previous studies rarely investigated the estimation of biomass for individual organs, such as the panicles in rice canopies, which led to the poor understanding of TLS technology in monitoring biomass partitioning among organs. The objective of this study was to investigate the potential of TLS in estimating the biomass for individual organs and aboveground biomass of rice and to examine the feasibility of developing universal models for the entire growing season. The field plots experiments were conducted in 2017 and 2018 and involved different nitrogen (N) rates, planting techniques and rice varieties. Three regression approaches, stepwise multiple linear regression (SMLR), random forest regression (RF) and linear mixed-effects (LME) modeling, were evaluated in estimating biomass with extensive TLS and biomass data collected at multiple phenological stages of rice growth across the entire season. The models were calibrated with the 2017 dataset and validated independently with the 2018 dataset.The results demonstrated that growth stage in LME modeling was selected as the most significant random effect on rice growth among the three candidates, which were rice variety, growth stage and planting technique. The LME models grouped by growth stage exhibited higher validation accuracies for all biomass variables over the entire season to varying degrees than SMLR models and RF models. The most pronounced improvement with a LME model was obtained for panicle biomass, with an increase of 0.74 in R2 (LME: R2 = 0.90, SMLR: R2 = 0.16) and a decrease of 1.15 t/ha in RMSE (LME: RMSE =0.79 t/ha, SMLR: RMSE =2.94 t/ha). Compared to SMLR and RF, LME modeling yielded similar estimation accuracies of aboveground biomass for pre-heading stages, but significantly higher accuracies for post-heading stages (LME: R2 = 0.63, RMSE =2.27 t/ha; SMLR: R2 = 0.42, RMSE =2.42 t/ha; RF: R2 = 0.57, RMSE =2.80 t/ha). These findings implied that SMLR was only suitable for the estimation of biomass at pre-heading stages and LME modeling performed remarkably well across all growth stages, especially for post-heading. The results suggest coupling TLS with LME modeling is a promising approach to monitoring rice biomass at post-heading stages at high accuracy and to overcoming the saturation of canopy reflectance signals encountered in optical remote sensing. It also has great potential in the monitoring of other crops in cloud-cover conditions and the instantaneous prediction of grain yield any time before harvest.

Leaf photosynthesis and stomatal conductance acclimate to elevated [CO2] and temperature thus increasing dry matter productivity in a double rice cropping system

Changes in atmospheric CO2 concentration ([CO2]) and temperature have impacts on leaf photosynthesis (Anet) and stomatal conductance (Gs) of rice, which strongly affect dry matter productivity. Understanding of the acclimation responses of Anet and Gs under elevated [CO2] and/or temperature is limited. A field experiment of double rice (early rice-late rice rotation) was conducted using open-top chambers from 2013 to 2016 in Hubei Province, Central China, with two levels of [CO2] (ambient, ambient + 60 μmol mol−1) and two levels of temperature (ambient, ambient + 2°C). Averaging across the four-year experiment, elevated [CO2] and elevated temperature increased Anet by 2.4 %–9.4 % and 2.4 %–10.7 %, respectively at pre-heading stages. A positive interaction was observed between elevated [CO2] and temperature in early rice which further increased Anet, while the interaction was not additive in late rice. Elevated [CO2] caused lower Gs and partly offset the stimulation of elevated temperature on Gs at tillering and jointing. At post-heading stages, photosynthetic acclimation to elevated [CO2] was observed as the stimulation of Anet was not continued. Elevated temperature decreased Anet by 1.7 %–16.6 % and further accelerated photosynthetic down-regulation by elevated [CO2]. Warming reduced Gs at milking and maturity in early rice because of high ambient temperature, which resulted in stomatal limitation to photosynthesis, but its effects on Gs in late rice were positive because the ambient temperature was low. Generally, Gs acclimated to warming and CO2 enrichment in parallel with Anet when there were no environmental constraints. Earlier leaf senescence, decreased leaf SPAD, lower seed-setting rate or larger sink capacity caused by elevated [CO2] and/or temperature might directly or indirectly explain the down-regulation on photosynthesis after heading. Our results show that warming alters the acclimation of leaf photosynthesis and stomatal conductance to CO2 enrichment and the combined effects differ between growth stages, and indicate that co-elevation of [CO2] and temperature may increase dry matter productivity in the Chinese double rice cropping system.

Differences in cadmium accumulation between indica and japonica rice cultivars in the reproductive stage

Excessive cadmium (Cd) in rice grains is of great concern worldwide, particularly in southern China where heavy metal pollution in the soil is widespread. Much work has been done regarding the key genes responsible for Cd absorption, transport, and accumulation in rice, but little is known about the differences of Cd accumulation between indica and japonica rice cultivars during the reproductive stage. Furthermore, physiological parameters, such as nonstructural carbohydrate content, involved in Cd accumulation have not been fully elucidated. We studied several indica and japonica cultivars under three different Cd treatment levels and harvested them at different periods after heading. Differences in Cd accumulation between subspecies mainly were generated during the reproductive stage. An increase in the Cd pollution level caused the average absorption rate of Cd in the aerial parts of the indica cultivars in the reproductive stage to be 6.17, 4.52, and 3.89 times greater than that of the japonica cultivars across the three Cd treatments. The contribution of Cd absorption by shoots to Cd accumulation at the pre- or postheading stages was 33.8% and 66.2% in indica, and 44.9% and 55.1% in japonica. We found a significant negative correlation between Cd content in the rice grains and the content of nonstructural carbohydrates in the sheath (P < 0.05). Cd translocation from sheath to grain occurred along with sugar transfer in the indica cultivars. The Cd content of the indica cultivar grain was 1.84–4.14 times higher than that of the japonica cultivars (P < 0.05). The japonica cultivars thus met the cereal Cd limits of China (0.2 mg kg−1) under low and moderate soil Cd pollution. These findings are helpful for the selection of proper cultivars and field management practices to alleviate Cd exposure risk in rice production.

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice “panicle line”—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the “panicle line” and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720–740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (θ) developed the PRDVI, which was obtained while using the “panicle line”, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

The accurate estimation of aboveground biomass (AGB) and leaf area index (LAI) is critical to characterize crop growth status and predict grain yield. Unmanned aerial vehicle (UAV) -based remote sensing has attracted significant interest due to its high flexibility and easiness of operation. The mixed effect model introduced in this study can capture secondary factors that cannot be captured by standard empirical relationships. The objective of this study was to explore the potential benefit of using a linear mixed-effect (LME) model and multispectral images from a fixed-wing UAV to estimate both AGB and LAI of rice. Field experiments were conducted over two consecutive years (2017–2018), that involved different N rates, planting patterns and rice cultivars. Images were collected by a compact multispectral camera mounted on a fixed-wing UAV during key rice growth stages. LME, simple regression (SR), artificial neural networks (ANN) and random forests (RF) models were developed relating growth parameters (AGB and LAI) to spectral information. Cultivar (C), growth stage (S) and planting pattern (P) were selected as candidates of random effects for the LME models due to their significant effects on rice growth. Compared to other regression models (SR, ANN and RF), the LME model improved the AGB estimation accuracy for all stage groups to varying degrees: the R2 increased by 0.14–0.35 and the RMSE decreased by 0.88–1.80 t ha−1 for the whole season, the R2 increased by 0.07–0.15 and the RMSE decreased by 0.31–0.61 t ha−1 for pre-heading stages and the R2 increased by 0.21–0.53 and the RMSE decreased by 0.72–1.52 t ha−1 for post-heading stages. Further analysis suggested that the LME model also successfully predicted within the groups when the number of groups was suitable. More importantly, depending on the availability of C, S, P or combinations thereof, mixed effects could lead to an outperformance of baseline retrieval methods (SR, ANN or RF) due to the inclusion of secondary effects. Satisfactory results were also obtained for the LAI estimation while the superiority of the LME model was not as significant as that for AGB estimation. This study demonstrates that the LME model could accurately estimate rice AGB and LAI and fixed-wing UAVs are promising for the monitoring of the crop growth status over large-scale farmland.

Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery

Crop aboveground biomass (AGB) is one of the most important indicators in crop breeding and crop management, and can be used for crop yield prediction. A number of vegetation indices (VIs) have been proposed to estimate crop biomass, but they perform poorly at high biomass levels and are easily affected by background materials. Texture analysis has been proved to be an efficient approach in forest biomass estimation, but has never been applied to crops with low-altitude unmanned aerial vehicle (UAV) images. The objective of this study was to improve rice AGB estimation by combining textural and spectral analysis of UAV imagery. A two-year rice experiment was conducted in 2015 and 2016, involving different nitrogen (N) rates, planting densities and rice cultivars with three replicates. A six-band multispectral (MS) camera was mounted on a UAV to acquire rice canopy images at critical stages during the rice growing seasons and concurrent field samplings were taken. Simple regression and stepwise multiple linear regression models were developed between biomass data from the two-year experiment and image parameters derived from four different types of feature sets. These features represented commonly used VIs, texture parameters, normalization of texture measurements (normalized difference texture index, NDTI) and combinations of VIs and NDTIs. Finally, all the regression models were evaluated by cross-validation over pooled data with the coefficient of determination (R2) and the root mean square error (RMSE). Results demonstrated that the optimized soil adjusted vegetation index (OSAVI) exhibited the best relationship with AGB for the whole season (R2 = 0.63) and post-heading stages (R2 = 0.65). Red-edge-based indices yielded best performance (R2 > 0.70) only for the growth stages before heading. The texture measurement mean (MEA) from the NIR band was the best among the eight candidates in AGB estimation. Texture index (NDTI (MEA800, MEA550)) was superior to all the evaluated VIs in estimating AGB for the whole season (R2 = 0.75) and pre-heading stages (R2 = 0.84). Further improvement was obtained across the whole season by combining NDTIs and VIs through a multiple linear regression. This multivariate model produced the highest estimation accuracy for all stages (R2 = 0.78 and RMSE = 1.84 t ha−1) and different stage groups (R2 = 0.84 and RMSE = 1.06 t ha−1 for pre-heading stages and R2 = 0.65 and RMSE = 1.94 t ha−1 for post-heading stages). The findings imply that the integration of textural information with spectral information significantly improves the accuracy for rice biomass estimation compared to the use of spectral information alone.

Exploring sub-daily to seasonal variations in methane exchange in a single-crop rice paddy in central Japan

Season-long methane (CH4) exchange was observed in a rice paddy field in central Japan (Kanto Region) using the eddy covariance technique to clarify the variations in environmental controls on CH4 exchange in different stages of cultivation. Before heading of rice plant, the CH4 emission depended on wind speed and soil temperature. The soil temperature dependence can be due to an increase in CH4 production, higher molecular diffusion, and higher conductance within rice plant at higher soil temperature. An occurrence of ebullitive emission was also suggested from the wind speed dependence. After heading was completed, relative humidity and water temperature influenced CH4 emission. The amplitude of the diurnal variation in emission increased from 0.03μmolm−2s−1 in the late pre-heading stage to 0.13μmolm−2s−1 in the post-heading stage. Induced convective throughflow within the rice aerenchyma after the change in plant structure was attributable to this variation in environmental controls after the heading. After drainage, CH4 emission was confined to short periods after strong rain events. The water level controlled the timing of emission, most likely by influencing the diffusion efficiency from the anoxic soil to the atmosphere and CH4 oxidation in the surface oxic zone. The variation in the dominant transport pathway needs to be accounted for in terrestrial ecosystem models to accurately predict CH4 emission from rice paddies.

Two β-amylase genes, OsBAM2 and OsBAM3, are involved in starch remobilization in rice leaf sheaths

To identify mechanisms of starch degradation in rice leaf sheaths at the post-heading stage, we investigated the function of OsBAM2 and OsBAM3, which encode plastid-targeted active β-amylase isoforms, in starch remobilization in leaf sheaths. The starch content in the second leaf sheaths below the flag leaf (the third leaf sheaths) peaked at the flag leaf emergence stage and gradually decreased until 15 days after heading. The mRNA levels of OsBAM2 and OsBAM3 in the third leaf sheaths increased from the flag leaf emergence stage to the heading stage when the starch content began to decrease. However, these mRNA levels did not always remain high during post-heading. Overexpression of OsBAM2 or OsBAM3 markedly repressed starch accumulation in the third leaf sheaths, showing that OsBAM2 and OsBAM3 function in starch degradation in rice leaf sheaths. In contrast, no significant differences in starch content in the third leaf sheaths were detected between knockdown plants of OsBAM2 or OsBAM3 and non-transgenic wild-type plants. Our results suggest that reduced expression of the individual genes, OsBAM2 or OsBAM3, does not result in excess accumulation of starch in the leaf sheaths, probably because of the complementary function of another gene or the action of other genes encoding starch-degrading .