Winter Wheat Biomass Research Articles

Evaluation of the soil profile quality of subsided land in a coal mining area backfilled with river sediment based on monitoring wheat growth biomass with UAV systems.

Underground coal mining leads to land subsidence, and the situation is particularly serious in the Coal-Grain Complex in eastern China, causing the crop production to be reduced or to be taken out. Backfilling with Yellow River sediment is one of the effective methods to solve the land subsidence in this area, but a key issue is how to select the optimal soil reconstruction profile so that the crop yield after backfilling and reclamation is unaffected. The main purpose of this study is to verify the feasibility of selecting the optimal soil reconstruction profile by rapid monitoring of crop growth and judging soil quality with the aid of unmanned aerial vehicle systems (UAVs). A control treatment and 13 experimental treatments were established for the study area. The control treatment consisted of using 30cm topsoil and 90cm subsoil and the topsoil is a proxy for native (undisturbed) soil from the study sites. All other treatments consisted of using varying combinations of subsoil and sediment overlain by 30cm of topsoil. The vegetation indices from the UAV multispectral images, and the plant height and vegetation coverage from the UAV RGB images were used for estimation of the winter wheat biomass in a random forest regression. The results showed that the random forest regression model yielded accurate estimation of the aboveground biomass. Furthermore, knowledge of plant height and vegetation coverage improved the accuracy of prediction such that crop growth was well characterized. The optimal soil profile consisted of 0.3m topsoil + 0.2m subsoil + 0.2m sediment + 0.2m subsoil + 0.3m sediment. A fast and effective airborne monitoring method for soil quality was established, thus providing greatly improved monitoring efficiency.

Evaluation of irrigation scheduling and yield response for wheat cultivars using the AquaCrop model in an arid climate

Abstract Yield, soil water balance components and evapotranspiration-based water productivity (WPET) of three winter wheat cultivars were investigated using the AquaCrop model under arid conditions in Shiraz, Iran, for two consecutive years. The irrigation treatments were non-stressed (I1) and post-anthesis water stress (I2) with three wheat cultivars. Evaluation of the model was performed using the coefficient of root mean squared error (RMSE), normalized RMSE and R2. The AquaCrop model performed well in simulating grain yield and final biomass production with R2 > 0.90, and RMSE and normalized RMSE values less than 10. The I1 treatment resulted in higher grain yield and biomass productivity than the I2 treatment. The I2 irrigation resulted in yield reduction of 21 and 24% in the 2006–2007 and 2007–2008 growing seasons, respectively, as compared with I2. Using the measured grain yield and AquaCrop-simulated water balance, the amount of WPET was found to vary from 0.68 to 0.95 kg m−3. The AquaCrop model was able to predict winter wheat biomass and yield production with a good accuracy in the arid conditions of this study and its ability to simulate these variables for different wheat cultivars' was especially notable. The AquaCrop model can be used to explore management scenarios to improve wheat water management in the study region.

Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

Improving winter wheat biomass and evapotranspiration simulation by assimilating leaf area index from spectral information into a crop growth model

Data assimilation, a state-of-the-art method that merges remote sensing data with a dynamic model to improve model performance, has been widely used in land surface process modeling. Application of data assimilation under various water conditions can provide insight of crop response to different water supply rates, which is useful for agricultural water management in arid and semi-arid regions. For this purpose, we developed a generic data assimilation methodology by integrating both the Shuffled Complex Evolution (SCE) and the Ensemble Kalman Filter (EnKF) algorithms into the Simple Algorithm For Yield and Evapotranspiration (SAFYE) model to provide improved simulation of winter wheat biomass and yield, and simulation of evapotranspiration (ET) under different water-supply scenarios. An experiment with nine irrigation scenarios was conducted during the 2013—2015 growing cycles. Field spectral data were employed to retrieve the leaf area index (LAI), which was then used as a single state variable to determine other parameters in the SAFYE model using a global optimization algorithm. Time-series LAI was eventually assimilated in the SAFYE model based on the EnKF algorithm to improve overall model simulation. The results showed that the simulated crop growth dynamics followed the measurements well in most cases when the estimated LAI was assimilated. The accuracy of simulated biomass at the daily step was improved, with the maximum RMSE decreased from 199.4 to 123.8 g m−2 and from 466.6 to 393.4 g m−2 for the 2013–2014 and 2014–2015 growing seasons respectively. A good agreement was also achieved between the estimated and field measured grain yield (R2 = 0.901, RMSE= 31.9 g m−2, RRMSE=6.55%) for both growing seasons. The simulation of soil water content in the top 0—20 cm soil layer was better (RMSE: 3.3—5.0 mm) than that of 0—100 cm layer (RMSE: 11.7—29.6 mm). Accuracy of the simulated ET under early-stage water deficit scenarios was lower than that under other scenarios, with a positive mean relative error of 14% (3.4—24.3%) during two growing seasons. This study demonstrates the great potential of coupling remote sensing data to improve the performance of SAFYE model in modeling winter wheat growth.

Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2

Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions.

Winter Wheat Resistant to Increases in Rain and Snow Intensity in a Semi-Arid System

As the atmosphere warms, precipitation events have been predicted and observed to become fewer and larger. Changes in precipitation patterns can have large effects on dryland agricultural production, but experimental tests on the effects of changing precipitation intensity are limited. Over 3 years, we tested the effects of increased precipitation intensity on winter wheat (Triticum aestivum L.; Promontory variety) in a temperate dryland agricultural system that was on a rotation of crop and fallow years. We used 11 (2.1 × 2.5 m) shelters to collect and redeposit rain and snow as larger, more intense events. Total precipitation was the same in all plots, but event sizes in each plot varied from 1 to 18 mm. Treatments increased soil water availability, but winter wheat biomass and grain yield did not differ among treatments. Similarly, other measured plant growth responses, including vegetation greenness, leaf area index, canopy temperature, photochemical efficiency, root area, and new root growth, did not differ among treatments. Results indicate that at least in the semiarid climate and silt loam soils studied here, anticipated increases in precipitation intensity are unlikely to affect winter wheat production negatively. Further, increased precipitation intensity may mitigate water stress caused by increasing temperatures and encourage the use of wheat varieties that utilize deeper, later season soil water.

Effects of row spacing and sowing rate on vertical distribution of photosynthetically active radiation, biomass, and grain yield in winter wheat canopy.

To clarify the effects of row spacing and sowing rate on the vertical distribution of canopy PAR, biomass, and grain yield in winter wheat, a field experiment was conducted without increa-sing water and fertilizer input. There were two row spacing modes, R1 (equal spacing, 20 cm+20 cm) and R2(wide and narrow row spacing, 12 cm+12 cm+12 cm+24 cm), and three sowing rates, D1 (low, 120 kg·hm-2), D2 (medium, 157.5 kg·hm-2), D3 (high, 195 kg·hm-2). The canopy photosynthetically active radiation (PAR) interception and utilization rate in different heights, population photosynthetic capacity, biomass, and grain yield were measured during the main growth stages of winter wheat. The results showed that both total PAR interception and upper layer PAR interception of winter wheat canopy under R1 treatment were significantly higher than those in R2 treatment, but those of the middle layer and lower layer were higher in R2 than in R1, and with significant difference in the middle layer. From flowering to maturity, the photosynthetic potential (LAD), population photosynthetic rate (CAP), PAR conversion rate, and utilization rate in R2 were all significantly higher than those in R1 under the same sowing rate, with the highest value under R2D2 treatment. With the increasing sowing rate, the population biomass (BA) and leaf biomass (BL) at different layers increased, but the individual biomass (BP) showed an opposite trend. Under the same sowing rate, BA, BL and BP in R2 were higher than that in R1 after the flowering stage. Among them, BA and BP had significant difference in row spacing treatments at the maturity stage, with significant difference between the two row spacing treatments being observed in BL of the middle and lower layers under D2 and D3 sowing rates. The spike number, grain number per spike, 1000-kernel weight, and grain yield of winter wheat among different treatments were the highest in R2D3, R2D1, R2D1, and R2D2, respectively. The 1000-kernel weight, grain number per spike and grain yield in R2 treatment were significantly higher than R1. In summary, the PAR interception in the middle and lower layers of winter wheat canopy was improved by changing row spacing, with positive consequence on the photosynthetic capacity of individual plant and population, PAR utilization and transformation efficiency, which finally increased biomass and grain yield. Therefore, optimizing the field structure and shaping the ideal population photosynthetic structure should pay more attention during the high-yield cultivation of winter wheat. Making full use of light resources per unit land area and excavating the photosynthetic production potential of crops were also critical to achieve high yield and efficiency. In this experiment, the population photosynthetic capacity, photosynthetic effective radiation utilization rate, and yield were the highest under the treatment of R2D2.

Effects of O3 pollution near formation on crop yield and economic loss

Abstract With the rapid development of industrialization and urbanization, a large number of ozone (O3) precursors such as nitrogen oxides and volatile organic compounds increase, which leads to the aggravation of O3 pollution in the atmosphere near the formation, which has an important impact and great harm on food crops, cash crops and natural vegetation. This paper aims to establish a regional evaluation method of O3 on winter wheat yield loss, reveal the status of winter wheat yield loss caused by O3 pollution, and propose important scientific countermeasures for food security and air pollution control. Based on the impact on the winter wheat field in the research of open top chamber type test data, and at the same time the surface O3 observation data and related meteorological data, detailed analysis of the surface of some areas of O3 concentration variation characteristics and its relationship with meteorological elements, analysis to determine the O3 on winter wheat photosynthesis and yield characteristics and dynamic changes of damage threshold, improved the O3 absorption of flux model, and established the O3 cumulated absorb effective dose and the relation model of winter wheat biomass and yield loss, improve the response model based on flux method of evaluation on the effect of O3 on crop yield. The results showed that for every 1 mmol O3 m−2 increase in effective O3 flux absorbed by stomatal accumulation in winter wheat leaves, the loss of ear weight was 5.2% compared with the control group. When the effective O3 flux of stomatal cumulative absorption in winter wheat leaves increased by 1 mmol O3 m−2, the total dry weight loss was 5.3% compared with the control group. When the effective O3 flux of stomatal cumulative absorption in winter wheat leaves increased by 1 mmol O3 m−2, the economic yield loss was 2.8% compared with the control group.

Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

Farming practices and deficit irrigation management improve winter wheat crop water productivity and biomass through mitigated greenhouse gas intensity under semi-arid regions.

Understanding the greenhouse gas emissions mechanism from the agricultural soils is essential to reach an agricultural system with a lower impact on the environment. The cultivation practices in combination with deficit irrigation have been used in a dry-land farming system to modify the soil water status. However, few research works have been focused on plastic film with deficit irrigation regimes on global warming potential (GWP), greenhouse gas intensity (GHGI), and biomass productivity under simulated rainfall conditions. In the current study, a 2-year study was carried out in a rainproof mobile shelter to study the potential role of two cultivation practices (i.e., furrow with plastic mulching on ridges, RF; and conventional flat cultivation, TF) in combination with two deficit irrigation regimes (i.e., 150 and 75 mm) and three simulated rainfall (i.e., 1, 275 mm; 2, 200 mm; and 3, 125 mm). . We found that RF2150 treatment was more effective in improving the soil water content, soil respiration rate, and winter wheat production and significantly reduced (39.2%) the GHGI and GWP than TF2150 treatment. The RF2150 treatment improved soil moisture and significantly increased (18.9%) grain yield, (11.1%) biomass, (75.8%) WUEg, and (64.1%) WUEb of winter wheat and largely mitigated GWP and GHGI. The RF system with 150-mm deficit irrigation regime plays a significant role in increasing the biomass productivity and soil respiration rate and minimizing the seasonal greenhouse gas fluxes, GHGI, and field ET rates under 200-mm precipitation condition. Compared with TF practice, the plastic film mulching on ridges and furrow on the planting zone could significantly improve biomass and WUE and reduce N2O, CO2, and CH4 emissions. The RF2150 treatment should be very good water-saving approach and a powerful tool to decrease GHGI and GWP via increased biomass, WUE, soil respiration rate, and wheat yields under a dry-land farming system.

Upscaling proximal sensor N-uptake predictions in winter wheat (Triticum aestivum L.) with Sentinel-2 satellite data for use in a decision support system

Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld Yara N-Sensor) during 2017 and 2018. Sentinel-2 satellite data from two processing levels (top-of-atmosphere reflectance, L1C, and bottom-of-atmosphere reflectance, L2A) were extracted and related to the proximal sensor data (n = 251). The utility of five vegetation indices for estimation of N-uptake was compared. A linear model based on the red-edge chlorophyll index (CI) provided the best N-uptake prediction (L1C data: r2 = 0.74, mean absolute error; MAE = 14 kg ha−1) when models were applied on independent sites and dates. Use of L2A data, rather than L1C, did not improve the prediction models. The CI-based prediction model was applied on all fields in an area with intensive winter wheat production. Statistics on N-uptake at the end of the stem elongation growth stage were calculated for 4169 winter wheat fields > 5 ha. Within-field variation in predicted N-uptake was > 30 kg N ha−1 in 62% of these fields. Predicted N-uptake was compared against N-uptake maps derived from tractor-borne Yara N-Sensor measurements in 13 fields (1.7–30 ha in size). The model based on satellite data generated similar information as the tractor-borne sensing data (r2 = 0.81; MAE = 7 kg ha−1), and can therefore be valuable in a DSS for variable-rate N application.

Determination of the most important meteorological parameters affecting the yield and biomass of barley and winter wheat using the random forest algorithm

Considering the very important role of climatic parameters on the yield and biomass of plants (especially in rain-fed agriculture), in this research, using data series of 9 stations during 1968–2017 the influence of 6 climatic parameters including the average of annual maximum and minimum temperature (Max-T and Min-T), the average of annual sunshine (Su-Sh), the average of annual relative humidity (H), the average of annual wind speed (Wi) and the average annual precipitation (P) on the yield and biomass of winter wheat (YWW and BWW) and barley (YB and BB) as two important and strategic species to provide the human food and the livestock feed in Iran, was investigated and prioritized. To assess the effectiveness rate of the climatic parameters on the response variables (YWW, BWW, YB and BB), the random forest algorithm (RF) was used. The results indicated that the RF algorithm had a good ability to predict the response variables because (1) the linear regression between simulated and predicted YWW, BWW, YB and BB using the AquaCrop model and the RF algorithm (respectively) had no difference with perfect reliable line (Y = X) in 0.05 or 0.01 significant levels and (2) R2 between simulated and predicted YWW, BWW, YB and BB was significant at 0.01 levels. According to the results, the P, Wi and Min-T parameters were the most influential climatic parameters on the YWW (respectively), the Min-T, Wi and P parameters were the most influential parameters on the YB and the Wi and Min-T parameters were the most influential parameters on the BWW and BB. On the other hand, in all stations, the Su-Sh and Hu were the least influential parameters on the YWW, BWW, YB and BB.

Optimizing nitrogen application for drip-irrigated winter wheat using the DSSAT-CERES-Wheat model

Improper application of nitrogen fertilizer for winter wheat production in the North China Plain (NCP) limits N use efficiency and increases the risk of non-point pollution. Crop simulation models may help with the assessment of N fertilizer management, to improve nitrogen use efficiency and to mitigate environmental pollution. In this study, the DSSAT-CERES-Wheat model was calibrated and validated with two-year field experiment, which coupled with five N rates (0, 120, 180, 240, and 300 kg ha−1) and three irrigation levels (40, 30, and 20 mm per irrigation) under drip irrigation. The calibrated DSSAT-CERES-Wheat model performed well in simulating the anthesis date (nRMSE=1.47%, d=0.89), maturity date (nRMSE=0.97%, d=0.92), shoot biomass (nRMSE=10.97%, d=0.92), grain yield (nRMSE=6.19%, d=0.96), and actual evapotranspiration (nRMSE=9.96%, d=0.76) of drip-irrigated winter wheat in two years. The calibrated model was subsequently used to evaluate drip-irrigated winter wheat production in response to six N rates (0, 120, 180, 240, 300, and 360 kg ha−1) under 40 meteorological scenarios, and the results showed that grain yields, water use efficiency, irrigation water use efficiency, and net margins increased with increasing N application rates until it reached 180 kg ha−1. Above 180 kg ha−1, negligible changes were measured in these values as the N rate increased. The nitrogen physiological efficiency and nitrogen partial factor productivity were greatly improved at a N rate of 180 kg ha−1, compared with those at a N rate of 240, 300, and 360 kg ha−1. Consequently, N rate of 180 kg ha−1 was the optimal nitrogen application rate for drip-irrigated winter wheat production. These results may provide a scientific basis for nitrogen management of drip-irrigated winter wheat in the NCP.

Mapping winter-wheat biomass and grain yield based on a crop model and UAV remote sensing

ABSTRACT Timely and accurately estimates of crop biomass and grain yield estimation are crucial for agricultural management. Optical remote sensing techniques can provide crop parameters (e.g., biomass, fractional vegetation cover (FVC)) at regional and larger scales. However, such techniques saturate at high crop canopy cover and cannot detect biomass stored in reproductive organs. The AquaCrop model can be used to estimate FVC, biomass, and grain yield output based on crop growth environmental parameters (e.g., temperature, rainfall, irrigation). In this work, we developed a method for estimating and mapping crop biomass and grain yield using unmanned aerial vehicle (UAV) remote sensing images and AquaCrop. The combination of low-cost UAV remote sensing data and AquaCrop can be used to map wheat biomass and grain yield before harvest. This work investigated whether biomass and grain yield can be predicted using measurements of biomass and FVC in several winter-wheat fields at an early growing stage using ground-based and remote sensing-based methods. FVC, biomass, and grain yield were measured at several key growing stages (jointing (S1), heading (S2), flowering (S3), grain filling (S4)) and harvest (H) during 2014–2015. We specifically evaluated the performances of using field- and UAV-based FVC and biomass measurements from different growing-stage combinations (e.g., S1–S4, S1–S3, and S1–S2) to calibrate the AquaCrop model and estimate biomass and grain yield. The results indicate that winter-wheat biomass and grain yield can be estimated by calibrating AquaCrop using (i) biomass and (ii) biomass and FVC. The results also reveal that the biomass and grain yield estimated using FVC and AquaCrop have poor accuracy compared with the biomass and grain yield estimated using (i) only biomass and (ii) biomass and FVC. The results suggest that the combined use of UAV remote sensing and AquaCrop can be used to obtain maps of biomass and biomass yield. When estimating winter-wheat biomass and grain yield one month (13 May) before harvest (11 June), the predicted biomass and grain yield agreed with the measured values (biomass: n = 96, coefficient of determination (R 2) = 0.61, mean absolute error (MAE) = 1.69 t ha–1, root-mean-square error (RMSE) = 2.10 t ha–1, normalized RMSE (nRMSE) = 18.5%; grain yield: n = 48, R 2 = 0.63, MAE = 0.96 t ha–1, RMSE = 1.16 t ha–1, nRMSE = 21.9%). When estimating winter-wheat biomass and grain yield one and a half months (26 April) before harvest (11 June), the predicted biomass and grain yield was lower than when estimating winter-wheat biomass and grain yield one month before harvest (biomass: n = 144, R 2 = 0.55, MAE = 2.52 t ha–1, RMSE = 3.34 t ha–1, nRMSE = 23.6%; grain yield: n = 48, R 2 = 0.34, MAE = 1.68 t ha–1, RMSE = 2.17 t ha–1, nRMSE = 35.7%).

Estimation of winter-wheat above-ground biomass using the wavelet analysis of unmanned aerial vehicle-based digital images and hyperspectral crop canopy images

ABSTRACT The crop above-ground biomass (AGB) is critically important for monitoring crop growth, and its accurate estimation can be used by agricultural managers to improve farmland management and to predict crop grain yield. Many studies have shown that models for the estimation of AGB for multiple crop growth stages based on optical remote sensing spectral indices (SIs) often underestimate the crop AGB in later growth stages due to saturation problems. The purpose of this study was to estimate winter-wheat AGB using (i) high-frequency information obtained from the image wavelet decomposition (IWD) of unmanned aerial vehicle (UAV)-based digital images of winter-wheat canopy (i.e. high-frequency IWD information), and (ii) variables obtained from the continuous wavelet transform (CWT) of hyperspectral images of winter-wheat canopy (i.e. hyperspectral CWT variables). Digital and hyperspectral images were acquired using a digital camera and an imaging spectrometer, both mounted on a UAV. Unlike optical SIs, high-frequency IWD information and hyperspectral CWT variables may not be limited by saturation problems for high winter-wheat canopy cover. Our results indicate that high-frequency IWD information and hyperspectral CWT variables both increase (decrease) with increasing winter-wheat AGB. A multiple linear stepwise regression technique was used to analyse the performance of (1) SIs, (2) CWT, (3) IWD, (4) SIs + CWT, (5) SIs + IWD, (6) CWT + IWD, and (7) SIs + CWT + IWD, for estimating AGB. Our results indicate that: (i) the method based only on optical SIs may not support the estimation of winter-wheat AGB for multiple growth stages (SIs: coefficient of determination (R 2) = 0.62, mean absolute error (MAE) = 1.30 t ha–1, root-mean-square error (RMSE) = 1.63 t ha–1); (ii) the combined use of the IWD of digital images of winter-wheat canopy and the CWT of hyperspectral images of crop canopy can support the estimation of winter-wheat AGB for multiple growth stages (CWT + IWD: R 2 = 0.85, MAE = 0.79 t ha–1, RMSE = 1.01 t ha–1), including later growth stages; and (iii) the combined use of SIs and the IWD of digital images of winter-wheat canopy can improve the estimation accuracy of winter-wheat AGB (SIs + IWD: R 2 = 0.80, MAE = 0.93 t ha–1, RMSE = 1.22 t ha–1), which may indicate that imaging spectrometers and cheap digital cameras have distinct advantages and can both be used to obtain AGB estimates using different methods. This work provides a new perspective on the use of high-frequency IWD information, hyperspectral CWT variables, and their combination to estimate AGB for multiple crop growth stages.

Effects of Water Stress on Photosynthesis, Yield, and Water Use Efficiency in Winter Wheat

Drought has become one of the major constraints to agricultural development, particularly in areas that lack water. Studying the effects of different water stresses on the photosynthesis, growth, yield, water use efficiency (WUE) and irrigation water productivity (IWP) of winter wheat will provide data for the development of scientific irrigation strategies for water-saving agricultural methods. According to the size of the field water capacity, four different water stress levels were set, i.e., 30–40% (severe stress), 40–50% (moderate stress), 50–60% (mild stress) and 60–80% (well-watered) of field water capacity, controlling the amount of irrigation through an automatic irrigation system. The results showed that the seasonal changes in photosynthetic parameters, such as net photosynthetic rate (Pn), intercellular carbon concentration (Ci), stomatal conductance (Gs) and transpiration (E), significantly decreased under moderate and severe stress. As a result, the height, biomass and grain size of winter wheat decreased significantly, which led to low WUE and IWP. The Pn of the mild stress group only slightly decreased compared to that of the well-watered group, and was actually higher during the flowering and grain-filling stages, resulting in increases in dry biomass and 1000 grain weight of 2.07% and 1.95%, respectively. Higher WUE and IWP were attributed to higher yields and less water use. Thus, mild stress (60–80% field water capacity) resulted in the optimal use of water resources without a significant reduction in yield in the North China Plain (NCP). Therefore, mild stress can be considered a suitable environment for winter wheat growth in arid areas.

Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat

Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.

Modelling Crop Biomass from Synthetic Remote Sensing Time Series: Example for the DEMMIN Test Site, Germany

This study compares the performance of the five widely used crop growth models (CGMs): World Food Studies (WOFOST), Coalition for Environmentally Responsible Economies (CERES)-Wheat, AquaCrop, cropping systems simulation model (CropSyst), and the semi-empiric light use efficiency approach (LUE) for the prediction of winter wheat biomass on the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site, Germany. The study focuses on the use of remote sensing (RS) data, acquired in 2015, in CGMs, as they offer spatial information on the actual conditions of the vegetation. Along with this, the study investigates the data fusion of Landsat (30 m) and Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m) data using the spatial and temporal reflectance adaptive reflectance fusion model (STARFM) fusion algorithm. These synthetic RS data offer a 30-m spatial and one-day temporal resolution. The dataset therefore provides the necessary information to run CGMs and it is possible to examine the fine-scale spatial and temporal changes in crop phenology for specific fields, or sub sections of them, and to monitor crop growth daily, considering the impact of daily climate variability. The analysis includes a detailed comparison of the simulated and measured crop biomass. The modelled crop biomass using synthetic RS data is compared to the model outputs using the original MODIS time series as well. On comparison with the MODIS product, the study finds the performance of CGMs more reliable, precise, and significant with synthetic time series. Using synthetic RS data, the models AquaCrop and LUE, in contrast to other models, simulate the winter wheat biomass best, with an output of high R2 (>0.82), low RMSE (<600 g/m2) and significant p-value (<0.05) during the study period. However, inputting MODIS data makes the models underperform, with low R2 (<0.68) and high RMSE (>600 g/m2). The study shows that the models requiring fewer input parameters (AquaCrop and LUE) to simulate crop biomass are highly applicable and precise. At the same time, they are easier to implement than models, which need more input parameters (WOFOST and CERES-Wheat).

How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery.

Winter wheat nitrogen utilization under different mulching practices on the Loess Plateau

AbstractWinter wheat (Triticum aestivum L.) is one of the most important cereal crops in the world, and mulching is mostly practiced in the semi‐arid region of Northwest China. We examined the effect of different mulching practices on soil N, plant N uptake, N use efficiency and yield of wheat with similar fertilizer rate. The experiment was conducted for 5 yr with four replicates. The five practices included: Control (CK), nitrogen fertilizer and no mulching (NM), nitrogen fertilizer plus plastic film mulching (PM), nitrogen fertilizer plus ridge mulching and furrow seeding (RM+FS), and nitrogen fertilizer plus straw mulching (SM). We found that yield and biomass of winter wheat were significantly affected by different mulching practices during the five growing seasons. Compared to the NM treatment, the PM, SM, and RM+FS treatments increased grain yield by 13, 9, and 4%, respectively, and biomass by 19, 13, and 10%, respectively. Compared to the NM, the RM+FS and PM treatments tended to decrease average grain, straw, and chaff N concentration, while straw mulching tended to increase average grain, straw, and chaff N concentration. Compared to the NM treatment, all mulching treatments consistently increased NUE, and straw mulching had the most significant effect. Nitrate‐N in the NM was most likely to leach into deep layers, followed by the SM and the lowest nitrate‐N leaching risk was in the two plastic film mulching treatments. The PM could increase winter wheat yield and decrease the risk of nitrate‐N leaching. Thus, PM performed better among the mulching practices.