Winter Wheat Biomass Research Articles

Влияние предпосевной обработки семян и агрохимикатов на урожайность озимой пшеницы

High yields cannot be achieved without the use of fertilizers, but how much they can pay off in modern economic realities and in drought conditions is unknown. In this regard, it is important to have new knowledge about effective systems of application of mineral fertilizers and agrochemicals in winter wheat cultivation in the zone of chestnut soils of the Lower Volga region. Therefore, the purpose of our research was to study the effect of growth stimulants in pre-sowing seed treatment and leaf fertilization with liquid fertilizers on the productivity of winter wheat in the Volgograd region. On average for 2021-2023 years, the minimum photosynthetic potential of winter wheat - 1288 thousand m2 day/ha was formed on plots without UAN 32 and growth regulators, and the highest - 2063 thousand m2 day/ha on plots with two fertilizers UAN 32 and growth regulator Emistim. Autumn fertilization with UAN 32 increased dry aboveground biomass of winter soft wheat in the variety Kamyshanka 6 at earing phase by 13%, two and three fertilizations by 26 and 27%, respectively. Growth regulator Polidon increased dry aboveground biomass of winter wheat at earing phase by 4%, Alfastim by 7%, Emistim by 12%. In the phase of milk ripeness dry biomass from growth regulator Polidon increased by 6%, Alfastim by 11%, Emistim by 14%. The lowest plant height of the variety Kamyshanka 6 was formed on the control - 0.89 m, the highest was on plots with two fertilizers and all variants on factor B and averaged 0.94 m, which is 0.05 m higher than the control. Spikelet length in the experiment was maximum in plots with two fertilizers and growth regulator Emistim. The minimum biological yield of winter wheat Kamyshanka 6 - 4.04 t/ha was obtained in the control variant, the maximum yield - 5.66 t/ha was observed in plots with two fertilizers UAN 32 and application of Emistim.

Improving the sustainability of arable cropping systems by modifying root traits: A modelling study for winter wheat

AbstractModifying root systems by crop breeding has been attracting increasing attention as a potentially effective strategy to enhance the sustainability of agriculture by increasing soil organic matter (SOM) stocks and soil quality, whilst maintaining or even improving yields. We used the new soil‐crop model USSF (Uppsala model of Soil Structure and Function) to investigate the potential of this management strategy using winter wheat as a model crop. USSF combines a simple (generic) crop growth model with physics‐based descriptions of soil water flow, water uptake and transpiration by plants. It also includes a model of the interactions between soil structure dynamics and organic matter turnover that considers the effects of physical protection and microbial priming on the decomposition of SOM. The model was first calibrated against field data on soil water contents and both above‐ground and root biomass of winter wheat measured during one growing season in a clay soil in Uppsala, Sweden using the GLUE method to identify five ‘acceptable’ parameter sets. We created four model crops (ideotypes) by modifying root‐related parameters to mimic winter wheat phenotypes with improved root traits. Long‐term (30‐year) simulations of a conventionally tilled monoculture of winter wheat were then performed to evaluate the potential effects of cultivating these ideotypes on the soil water balance, soil organic matter stocks and grain yields. Our results showed that ideotypes with deeper root systems or root systems that are more effective for water uptake increased grain yields by 3% and SOM stocks in the soil profile by ca. 0.4%–0.5% in a 30‐year perspective (as an average of the five parameter sets). An ideotype in which below‐ground allocation of dry matter was increased at the expense of stem growth gave even larger increases in SOM stocks (ca. 1.4%). An ideotype combining all three modifications (deeper and more effective root systems and greater root production) showed even more promising results: compared with the baseline scenario, surface runoff decreased while yields were predicted to increase by ca. 7% and SOM stocks in the soil profile by ca. 2%, which is roughly equivalent to ca. 20% of the 4‐per‐mille target (https://4p1000.org/).

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

CFD MODELING OF THE COMBUSTION OF UKRAINIAN AND BIOMASS IN A FLARE BOILER UNIT TPP-210a

To increase the amount of energy produced with the bioresources use is the most important step in environmentally friendly electricity production. An analysis of the computer modeling results of pellet and biomass burning processes in the furnace chamber of the TPP-210a steam boiler was carried out. The scientific novelty of the work consists of determining the possibility and perspective of using non-design types of fuel, such as gas coal of domestic production and solid biomass (instead of anthracite), as a raw material base of existing boiler units. On the basis of this analysis, it was determined that the model allows to quite accurately assess the aerodynamic structure of the flow in the burners and the boiler furnace. Such a precise assessment is possible if the thermophysical properties of the burning fuel are accurately specified. The average integral temperature of the combustion products in the fuel during the gas coal burning is higher than while burning other fuels in the form of pellets from the sunflower husk and from biomass of spring wheat and winter wheat. The biomass used in the modeling has a similar chemical composition; therefore, the temperature distributions in the case of their burning are also similar. Subsequently, the results of the research can be used to increase the efficiency of the combustion process when burning biofuel and to modernize fuel-burning systems of low-power boilers of industrial and municipal thermal engineering, the housing-and-utilities sector, etc.

Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing

Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing

USING UAV-DERIVED PLANT HEIGHT AS AN ESTIMATOR FOR BIOMASS AND N-UPTAKE

Abstract. In Precision Agriculture applications, the knowledge of the N-uptake of crops in a spatial context is a precondition for precise N-fertilization. Therefore, non-destructive methods to determine N concentration or N-uptake of crops are of key interest. In this contribution, we will present an approach based on UAV-based crop height analysis to estimate N-uptake. The hypothesis is that biomass can be very well estimated using UAV-derived crop height. It is also known that biomass is highly correlated with N-uptake. Hence, we propose to use UAV-derived crop height as a direct estimator for N-uptake. We will present results from a winter wheat field trial comprising six varieties, three different N amounts, and five repetitions. Multitemporal models have been created from data of the growing season 2020 with a promising performance for biomass (R2 = 0.91) and N-uptake (R2 = 0.72). The two linear regression models have been applied to a similar independent data set from the following year, 2021. The results are promising for biomass (R2 = 0.82) and N-uptake (R2 = 0.75). Based on our findings, we conclude that UAV-derived crop height - as a structural crop trait - can robustly serve as an estimator for biomass and N-uptake in winter wheat.

Using structure-from-motion to estimate cover crop biomass and characterize canopy structure

ContextVariability in biomass production poses a challenge for growers when using cover crops for weed control. However, most methods for assessing cover crop biomass are laborious and impractical on a field scale. ObjectiveThe goal of the present study was to determine the feasibility of using Structure-from-Motion (SfM) photogrammetry to estimate biomass in cereal rye (Secale cereale L.) and winter wheat (Triticum aestivum L.) cover crops by correlating biomass with 3-D point cloud pixel density and crop height. MethodsPoint clouds were generated using a SfM algorithm from RGB (red, green, and blue) videos collected by a hand-held GoPro camera over sixteen crop fields in North Carolina, Iowa, and Maryland, USA, throughout two growing seasons (2021–2023). Crop height, leaf area index (LAI), and photosynthetically active radiation (PAR) were also measured. ResultsBiomass was positively correlated with crop height for both cereal rye (R2 = 0.621) and wheat (R2 = 0.55). LAI was positively correlated with biomass accumulation and crop height for both species, increasing linearly in rye and exponentially in wheat. Conversely, PAR penetration below the canopy decreased with biomass accumulation and crop height in both species, with a more rapid extinction in wheat than rye. Point cloud pixel density showed a positive linear relationship with biomass in rye but saturated after 2.5 tonnes ha−1 (2500 kg ha−1). In wheat, point cloud pixel density was weakly and negatively correlated with biomass due to a denser canopy causing faster saturation of tissue detection by SfM point clouds. However, considering crop height and point cloud density integrating them both in the model allowed obtaining a positive relationship with biomass through levels of 8 tonnes ha−1 (8000 kg ha−1) in both species. When models were validated with independent data, predicted and measured biomass were positively correlated for both rye (R2 = 0.86) and wheat (R2 = 0.78). ConclusionsBased on the results, using SfM to generate 3-D point clouds can provide a more accurate estimation of biomass than canopy height alone by capturing species-level differences in canopy architecture. ImplicationsThe results of this study suggest that SfM can potentially be used as a non-destructive tool for growers to monitor biomass production in cereal cover crops other systems such as energy/forage crops, which can help inform management decisions and conserve resources.

Plot Layout Method of Field Experiment for Wheat with Border Irrigation Based on Soil Water Content Heterogeneity

The objective of this research was to improve the accuracy and representativeness of experimental plot studies by determining the optimum plot area and replication number for winter wheat with border irrigation. Considering the spatial distribution of soil water content, the border effect in relation to crop growth, and the lateral seepage of soil water, we sought to study and optimize the area and specifications of irrigation experiment plots with different levels and replicates. The results show that the experimental irrigation plot consisted of two parts—the core area and the guard area. The most suitable area for the experiment plot core area, with a single level and without replicates, was 60–80 m2. The core experimental area can be arranged with two replicates per 40 m2, with differences in soil moisture content between the treatments reaching more than 15% at the two experiment levels. Each plot comprised two replicates, or if they were 20 m2, then they contained three replicates; when the soil moisture contents differed between 10% and 15%, the area of each replicate plot was 80 m2, comprising two replicates, or 30 m2 with three replicates. When the difference in soil moisture content between the treatments exceeded 15% with the three experimental levels, the area of each plot was 30 m2 and they contained two replicates, or 20 m2 containing three replicates; at differences of 10% to 15%, each replicate plot was 50 m2 containing two replicates, or 30 m2 with three replicates. The experimental plots were rectangular, with irrigation furrows dug lengthwise; therefore, the plots had aspect ratios between 7:1 and 5:1. The width of the buffer area was over 60 cm. The effect of the border on plant height and LAI for winter wheat primarily emerged with one to three rows (20–60 cm) at the jointing stage, while the effect on grain yield and biomass in winter wheat mainly emerged with one to two rows (20–40 cm). The conclusions of this research will inform the development of surface irrigation methods for silt loam in northern Henan, as a reference for optimizing experiment plots employing border irrigation with different soil textures.

Benefit of aerosol reduction to winter wheat during China's clean air action: A case study of Henan Province

A strongly declining aerosol radiative effect has been observed in China since 2013 after implementing the clean air action, yet its impact on wheat (Triticum aestivum L.) production remains unclear. We use satellite measures and a biophysical crop model to assess the impact of aerosol-induced radiative perturbations on winter wheat production in the agricultural belt of Henan province from 2013 to 2018. After calibrating parameters with the extended Fourier Amplitude Sensitivity Test (EFAST) and the generalized likelihood uncertainty estimation (GLUE) method, the DSSAT CERES-Wheat model was able to simulate crop biomass and yield more accurately. We found that the aerosol negatively impacted wheat biomass by 21.87% and yield by 22.48% from 2006 to 2018, and the biomass effects from planting to anthesis were more significant compared to anthesis to maturity. Due to the strict clean air action, under all-sky conditions, the surface solar shortwave radiation (SSR) in 2018 increased by about 7.08% over 2006-2013 during the wheat growing seasons. As a result of the improvement of crop photosynthesis, winter wheat biomass and yield increased by an average of 5.46% and 2.9%, respectively. Our findings show that crop carbon uptake and yield will benefit from the clean air action in China, helping to ensure national food and health security.

A Three-Dimensional Conceptual Model for Estimating the Above-Ground Biomass of Winter Wheat Using Digital and Multispectral Unmanned Aerial Vehicle Images at Various Growth Stages

The timely and accurate estimation of above-ground biomass (AGB) is crucial for indicating crop growth status, assisting management decisions, and predicting grain yield. Unmanned aerial vehicle (UAV) remote sensing technology is a promising approach for monitoring crop biomass. However, the determination of winter wheat AGB based on canopy reflectance is affected by spectral saturation effects. Thus, constructing a generic model for accurately estimating winter wheat AGB using UAV data is significant. In this study, a three-dimensional conceptual model (3DCM) for estimating winter wheat AGB was constructed using plant height (PH) and fractional vegetation cover (FVC). Compared with both the traditional vegetation index model and the traditional multi-feature combination model, the 3DCM yielded the best accuracy for the jointing stage (based on RGB data: coefficient of determination (R2) = 0.82, normalized root mean square error (nRMSE) = 0.2; based on multispectral (MS) data: R2 = 0.84, nRMSE = 0.16), but the accuracy decreased significantly when the spike organ appeared. Therefore, the spike number (SN) was added to create a new three-dimensional conceptual model (n3DCM). Under different growth stages and UAV platforms, the n3DCM (RGB: R2 = 0.73–0.85, nRMSE = 0.17–0.23; MS: R2 = 0.77–0.84, nRMSE = 0.17–0.23) remarkably outperformed the traditional multi-feature combination model (RGB: R2 = 0.67–0.88, nRMSE = 0.15–0.25; MS: R2 = 0.60–0.77, nRMSE = 0.19–0.26) for the estimation accuracy of the AGB. This study suggests that the n3DCM has great potential in resolving spectral errors and monitoring growth parameters, which could be extended to other crops and regions for AGB estimation and field-based high-throughput phenotyping.

Effects of Biochar Application on Soil Bacterial Community Diversity and Winter Wheat Growth in Wheat Fields

A long-term field experiment was conducted to study the diversity of soil bacterial communities and the response of crop growth to biochar application, in order to provide a scientific basis for the rational application of biochar in agricultural fields. Four treatments were applied at 0 (B0 blank), 5 (B1), 10 (B2), and 20 t·hm-2(B3) to investigate the effects of biochar on soil physical and chemical properties, soil bacterial community diversity, and growth of winter wheat using Illumina MiSeq high-throughput sequencing technology. The results showed that soil water content, pH value, soil organic carbon, total nitrogen, nitrate nitrogen content, winter wheat biomass, nitrogen uptake, and yield showed an increasing trend with the increase in biochar amount. The high-throughput sequencing results showed that the B2 treatment significantly reduced the alpha diversity of the bacterial community at the flowering stage. The overall response of soil bacterial community composition to different application rates of biochar and phenological phases was taxonomically consistent. In this study, Proteobacteria, Acidobacteria, Planctomycetes, Gemmatimonadetes, and Actinobacteria were the dominant bacterial phyla. The relative abundance of Acidobacteria decreased, but the relative abundance of Proteobacteria and Planctomycetes increased with biochar application. The results of redundancy analysis, co-occurrence network analysis, and PLS-PM analysis indicated that bacterial community compositions were closely associated with soil parameters such as soil nitrate and total nitrogen. The average connectivity between 16S OTUs was higher under the B2 and B3 treatments (16.966 and 14.600) than under the B0 treatment. The variation in soil bacterial community (89.1%) was regulated by biochar and sampling period and partly explained the changes in the growth dynamics of winter wheat (0.077). In conclusion, biochar application could regulate the changes in the soil bacterial community and promote crop growth after seven years of application. It is suggested that 10-20 t·hm-2 biochar should be applied in semi-arid agricultural areas to achieve sustainable agricultural development.

Evaluation of Hyperspectral Monitoring Model for Aboveground Dry Biomass of Winter Wheat by Using Multiple Factors

The aboveground dry biomass (AGDB) of winter wheat can reflect the growth and development of winter wheat. The rapid monitoring of AGDB by using hyperspectral technology is of great significance for obtaining the growth and development status of winter wheat in real time and promoting yield increase. This study analyzed the changes of AGDB based on a winter wheat irrigation experiment. At the same time, the AGDB and canopy hyperspectral reflectance of winter wheat were obtained. The effect of spectral preprocessing algorithms such as reciprocal logarithm (Lg), multiple scattering correction (MSC), standardized normal variate (SNV), first derivative (FD), and second derivative (SD); sample division methods such as the concentration gradient method (CG), the Kennard–Stone method (KS), and the sample subset partition based on the joint X–Y distances method (SPXY); sample division ratios such as 1:1 (Ratio1), 3:2 (Ratio2), 2:1 (Ratio3), 5:2 (Ratio4), and 3:1 (Ratio5); dimension reduction algorithms such as uninformative variable elimination (UVE); and modeling algorithms such as partial least-squares regression (PLSR), stepwise multiple linear regression (SMLR), artificial neural network (ANN), and support vector machine (SVM) on the hyperspectral monitoring model of winter wheat AGDB was studied. The results showed that irrigation can improve the AGDB and canopy spectral reflectance of winter wheat. The spectral preprocessing algorithm can change the original spectral curve and improve the correlation between the original spectrum and the AGDB of winter wheat and screen out the bands of 1400 nm, 1479 nm, 1083 nm, 741 nm, 797 nm, and 486 nm, which have a high correlation with AGDB. The calibration sets and validation sets divided by different sample division methods and sample division ratios have different data-distribution characteristics. The UVE method can obviously eliminate some bands in the full-spectrum band. SVM is the best modeling algorithm. According to the universality of data, the better sample division method, sample division ratio, and modeling algorithm are SPXY, Ratio4, and SVM, respectively. Combined with the original spectrum and by using UVE to screen bands, a model with stable performance and high accuracy can be obtained. According to the particularity of data, the best model in this study is FD-CG-Ratio4-Full-SVM, for which the R2c, RMSEc, R2v, RMSEv, and RPD are 0.9487, 0.1663 kg·m−2, 0.7335, 0.3600 kg·m−2, and 1.9226, respectively, which can realize hyperspectral monitoring of winter wheat AGDB. This study can provide a reference for the rational irrigation of winter wheat in the field and provide a theoretical basis for monitoring the AGDB of winter wheat by using hyperspectral remote sensing technology.

Enhanced soil water recovery and crop yield following conversion of 9-year-old leguminous pastures into croplands

Pasture-crop rotation has been considered as an efficient agronomical practice to improve soil and water conservation, develop livestock production, and promote food security. However, the characteristics of soil water restoration and performances of crop yield and water use efficiency (WUE) after conversion of long-term leguminous pasture into croplands still remain unclear. A field experiment was conducted in China’s Loess Plateau to investigate soil water restoration and wheat yields in crop rotation with Italian ryegrass (Festuca perennis Lam.) and winter wheat (Triticum aestivum L.) converted from three 9-year-old leguminous pastures, viz. milk vetch (Astragalus adsurgens Pall.), alfalfa (Medicago sativa L.) and bush clover (Lespedeza davurica S.), and unplanted control (fallow). Soil water in the alfalfa treatment gradually recovered from up to down compared to the fallow treatment (control) during cultivation phase. After one year of rotation, the recovery levels of soil water in the three pasture-crop rotation systems at 0–500 cm soil depth were 97.1%, 95.5% and 94.2%, respectively, compared to the fallow treatment. The legume treatments significantly enhanced the crop yield (35.6%−64.5%), aboveground biomass (28.7%−59.4%) and WUE (60%−68.3%) of winter wheat compared with fallow. These results imply that the concerns about soil desiccation from long-term alfalfa was resolvable after long-term alfalfa converted into cropland in the rainfed farming area of Loess Plateau. The results also suggest that legume pasture-crop rotation can facilitate achieving a sustainable development of rainfed farming.

Nitrogen Mitigates Salt Stress and Promotes Wheat Growth in the Yellow River Delta, China

Salt-affected soils is an important soil resource. Understanding fertilizer and salinity interaction are of great economic importance for improving crop yield and fertilizer use efficiency. A pot experiment was carried out to study the application of nitrogen (N) for ameliorating salt stress in wheat grown in the coastal saline soil of the Yellow River delta. Several controlling levels of salinity and nitrogen (0.7, 1.7, 2.7 g/kg, and 135, 270, and 405 kg/ha) were designed in a pot experiment in a stable water content state to investigate the N and salt interaction on soil properties and winter wheat growth characteristics. The results showed that the dry weight of winter wheat was promoted by salinity in the early growth stage (20 days), then it was gradually inhibited by nitrogen fertilizer. When winter wheat was grown by 54 days, the N and salinity had significant effects on the biomass of winter wheat. The nitrogen content of wheat shoot and root was mainly affected by N addition usage, and the largest value was obtained in 270 kg/ha N dosage treatments. The higher the salt content existed in the soil, the lower the growth rate shown in wheat cultivation. Under saline conditions, the N fertilizer application amount should be controlled to no more than 270 kg/ha, so that it could greatly promote wheat growth. Reasonable fertilizer usage could significantly contribute to crop yield and food quality of the saline agriculture in the Yellow River delta.

Application and Evaluation of a Simple Crop Modelling Framework: A Case Study for Spring Barley, Winter Wheat and Winter Oilseed Rape over Ireland

Globally, croplands represent a significant contributor to climate change, through both greenhouse gas emissions and land use changes associated with cropland expansion. They also represent locations with significant potential to contribute to mitigating climate change through alternative land use management practices that lead to increased soil carbon sequestration. In spite of their global importance, there is a relative paucity of tools available to support field- or farm-level crop land decision making that could inform more effective climate mitigation practices. In recognition of this shortcoming, the Simple Algorithm for Yield Estimate (SAFY) model was developed to estimate crop growth, biomass, and yield at a range of scales from field to region. While the original SAFY model was developed and evaluated for winter wheat in Morocco, a key advantage to utilizing SAFY is that it presents a modular architecture which can be readily adapted. This has led to numerous modifications and alterations of specific modules which enable the model to be refined for new crops and locations. Here, we adapted the SAFY model for use with spring barley, winter wheat and winter oilseed rape at selected sites in Ireland. These crops were chosen as they represent the dominant crop types grown in Ireland. We modified the soil–water balance and carbon modules in SAFY to simulate components of water and carbon budgets in addition to crop growth and production. Results from the modified model were evaluated against available in situ data collected from previous studies. Spring barley biomass was estimated with high accuracy (R2 = 0.97, RMSE = 95.8 g·m−2, RRMSE = 11.7%) in comparison to GAI (R2 = 0.73, RMSE = 0.44 m2·m−2, RRMSE = 10.6%), across the three years for which the in situ data was available (2011–2013). The winter wheat module was evaluated against measured biomass and yield data obtained for the period 2013–2015 and from three sites located across Ireland. While the model was found to be capable of simulating winter wheat biomass (R2 = 0.71, RMSE = 1.81 t·ha−1, RRMSE = 8.0%), the model was found to be less capable of reproducing the associated yields (R2 = 0.09, RMSE = 2.3 t·ha−1, RRMSE = 18.6%). In spite of the low R2 obtained for yield, the simulated crop growth stage 61 (GS61) closely matched those observed in field data. Finally, winter oilseed rape (WOSR) was evaluated against a single growing season for which in situ data was available. WOSR biomass was also simulated with high accuracy (R2 = 0.99 and RMSE = 0.52 t·ha−1) in comparison to GAI (R2 = 0.3 and RMSE = 0.98 m2·m−2). In terms of the carbon fluxes, the model was found to be capable of estimating heterotrophic respiration (R2 = 0.52 and RMSE = 0.28 g·C·m−2·day−1), but less so the ecosystem respiration (R2 = 0.18 and RMSE = 1.01 g·C·m−2·day−1). Overall, the results indicate that the modified model can simulate GAI and biomass, for the chosen crops for which data were available, and yield, for winter wheat. However, the simulations of the carbon budgets and water budgets need to be further evaluated—a key limitation here was the lack of available in situ data. Another challenge is how to address the issue of parameter specification; in spite of the fact that the model has only six variable crop-related parameters, these need to be calibrated prior to application (e.g., date of emergence, effective light use efficiency etc.). While existing published values can be readily employed in the model, the availability of regionally derived values would likely lead to model improvements. This limitation could be overcome through the integration of available remote sensing data using a data assimilation procedure within the model to update the initial parameter values and adjust model estimates during the simulation.

Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index

Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and high-throughput observations, spectral technology provides a feasible method for obtaining LAI and above-ground biomass of crops. In the present study, the spectral, LAI and above-ground biomass data of winter wheat were collected, and 7 species (14 in total) were calculated based on the original and first-order differential spectrum correlation spectral indices with LAI. Then, the correlation matrix method was used for correlation with LAI. The optimal wavelength combination was extracted, and the results were calculated as the optimal spectral index related to LAI. The calculation process of the optimal spectral index related to above-ground biomass was the same as that aforementioned. Finally, the optimal spectral index was divided into three groups of model input variables, winter wheat LAI and above-ground biomass estimation models were constructed using support vector machine (SVM), random forest (RF) and a back propagation neural network (BPNN), and the models were verified. The results show that the correlation coefficient between the highest of the optimal spectral indices, the LAI, and the above-ground biomass of winter wheat exceeded 0.6, and the correlation was good. The methods for establishing the optimal estimation models for LAI and above-ground biomass of winter wheat are all modeling methods in which the input variables are the combination of the first-order differential spectral index (combination 2) and RF. The R2 of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R2 of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters.

Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations

Abstract. Biomass is an important indicator in the ecological and management process that can now be estimated at higher temporal and spatial resolutions because of unmanned aircraft systems (UAS). LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Its signals are capable of penetrating through vegetation canopies enabling the capture of more information along the plant structure. Separate studies have used LiDAR for crop height, rate of canopy penetrations as related to leaf area index (LAI), and signal intensity as an indicator of plant chlorophyll status or green area index (GAI). These LiDAR products are combined within a machine learning method such as an artificial neural network (ANN) to assess the potential in making accurate biomass estimations for winter wheat.

Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV

One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from the middle to late growth stages. This study focuses on combining VIs acquired by a consumer-grade multiple-spectral UAV and machine learning regression techniques to (i) determine the optimal time window for AGB estimation of winter wheat and to (ii) determine the optimal combination of multi-spectral VIs and regression algorithms. UAV-based multi-spectral data and manually measured AGB of winter wheat, under five nitrogen rates, were obtained from the jointing stage until 25 days after flowering in the growing season 2020/2021. Forty-four multi-spectral VIs were used in the linear regression (LR), partial least squares regression (PLSR), and random forest (RF) models in this study. Results of LR models showed that the heading stage was the most suitable stage for AGB prediction, with R2 values varying from 0.48 to 0.93. Three PLSR models based on different datasets performed differently in estimating AGB in the training dataset (R2 = 0.74~0.92, RMSE = 0.95~2.87 t/ha, MAE = 0.75~2.18 t/ha, and RPD = 2.00~3.67) and validation dataset (R2 = 0.50~0.75, RMSE = 1.56~2.57 t/ha, MAE = 1.44~2.05 t/ha, RPD = 1.45~1.89). Compared with PLSR models, the performance of the RF models was more stable in the prediction of AGB in the training dataset (R2 = 0.95~0.97, RMSE = 0.58~1.08 t/ha, MAE = 0.46~0.89 t/ha, and RPD = 3.95~6.35) and validation dataset (R2 = 0.83~0.93, RMSE = 0.93~2.34 t/ha, MAE = 0.72~2.01 t/ha, RPD = 1.36~3.79). Monitoring AGB prior to flowering was found to be more effective than post-flowering. Moreover, this study demonstrates that it is feasible to estimate AGB for multiple growth stages of winter wheat by combining the optimal VIs and PLSR and RF models, which overcomes the saturation problem of using individual VI-based linear regression models.

Дистанційне визначення величини фотосинтетичного стоку органічного вуглецю (Ph) в агроценозі пшениці озимої залежно від строкатості ґрунтового покриву

The article presents the results of research on the relationship between soil differentiation and remote identification of the amount of photosynthetic runoff of organic carbon (Ph) in winter wheat crops. scientific work is devoted to the development of new and improvement of existing models of accumulation of biomass of agricultural crops, in order to form the structure of these models under different climatic conditions and on different types of soils. Models were developed during the research of carbon runoff values in the agrocenosis of winter wheat on sandy and light loam soils (PhСО2LIG and PhСLIG), medium and heavy loam soils (PhСО2HEAV and PhСHEAV) have been developed, and their accuracy has been assessed. Experimental studies have established and confirmed territorial consistency of the values of organic carbon runoff PhСО2LIG on wheat crops with geomorphological features of the quality of sandy and loamy soils, in particular the particle size distribution, gleying of soil and gley soil, was revealed. It is established that 95.2 % of the value of the error of remote identification of Ph of winter wheat biomass is due to the values of grain yield, straw and root mass. Taking into account the peculiarities of diagnostic phases of photosynthetic runoff of organic carbon phases of organogenesis of winter wheat for remote identification, the recommended average annual time windows of the Sentinel 2A image order in terms of natural and climatic zones of Ukraine have been developed. Thus, it is proposed to develop similar models for other satellites that produce images in the SWIR and NIR bands close to 2080-2350 nm and 1550-1750 nm, respectively. This will provide more images needed to diagnose carbon runoff in winter wheat crops.

Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

The main goal of this investigation was to evaluate the potential of the WOFOST model for estimating leaf area index (LAI), actual evapotranspiration (ETa), soil moisture content (SM), above-ground biomass levels (TAGP) and grain yield (TWSO) of winter wheat in the semi-arid region of Tensift Al Haouz, Marrakech (central Morocco). An application for the estimation of the Yield Gap is also provided. The model was firstly calibrated based on three fields data during the 2002–2003 and 2003–2004 growing seasons, by using the WOFOST implementation in the Python Crop simulation Environment (PCSE) to optimize the different parameters that provide the minimum difference between the measured and simulated LAI, TAGP, TWSO, SM and ETa. Then, the model validation was performed based on the data from five other wheat fields. The results obtained showed a good performance of the WOFOST model for the estimation of LAI during both growing seasons on all validation fields. The average R2, RSME and NRMSE were 91.4%, 0.57 m2/m2, and 41.4%, respectively. The simulated ETa dynamics also showed a good agreement with the observations by eddy covariance systems. Values of 60% and 72% for R2, 0.8 mm and 0.7 mm for RMSE, 54% and 31% for NRMSE are found for the two validation fields, respectively. The model’s ability to predict soil moisture content was also found to be satisfactory; the two validation fields gave R2 values equal to 48% and 49%, RMSE values equal to 0.03 cm3/cm3 and 0.05 cm3/cm3, NRMSE values equal to 11% and 19%. The calibrated model had a medium performance with respect to the simulation of TWSO (R2 = 42%, RSME = 512 kg/ha, NRMSE = 19%) and TAGP (R2 = 34% and RSME = 936 kg/ha, NRMSE = 16%). After accurate calibration and validation of the WOFOST model, it was used for analyzing the gap yield since this model is able to estimate the potential yield. The WOFOST model allowed a good simulation of the potential yield (7.75 t/ha) which is close to the optimum value of 6.270 t/ha in the region. Yield gap analysis reveals a difference of 5.35 t/ha on average between the observed yields and the potential yields calculated by WOFOST. Such difference is ascribable to many factors such as the crop cycle management, agricultural practices such as water and fertilization supply levels, etc. The various simulations (irrigation scenarios) showed that early sowing is more adequate than late sowing in saving water and obtaining adequate grain yield. Based on various simulations, it has been shown that the early sowing (mid to late December) is more adequate than late sowing with a total amount of water supply of about 430 mm and 322 kg (140 kg of N, 80 kg of P and 102 kg of K) of fertilization to achieve the potential yield. Consequently, the WOFOST model can be considered as a suitable tool for quantitative monitoring of winter wheat growth in the arid and semi-arid regions.