Aboveground Crop Biomass Research Articles

Genome-wide association analysis reveals novel candidate loci and a gene regulating tiller number in orchardgrass

Tillers are specialized lateral shoots arising from axillary buds at basal nodes, and are also an important agronomic trait that determines the aboveground biomass and grain yield of various gramineous crops. So far, few genes have been reported to control tiller formation and most have been in the annual crop rice (Oryza sativa). Orchardgrass (Dactylis glomerata) is an important perennial forage crop with great economic and ecological value, but its genes regulating tillering have remained largely unknown. In the present study, we used a natural population of 264 global orchardgrass germplasms to determine genes associated with quantitative variation in tiller number through genome-wide association study analysis. A total of 19 putative loci and 55 genes associated with tiller number were thus identified. Additionally, 26 putative differentially expressed genes with tiller number, including DgCYC-C1, were identified by RNA-seq and genome-wide association study analysis. DgCYC-C1 which is involved in cell division, was overexpressed, revealing that DgCYC-C1 positively regulates tiller number. These results provide some new candidate genes or loci for the improvement of tiller number in crops, which might advance new sustainable strategies to meet global crop production challenges.

Soil mineral nitrogen and winter wheat nitrogen productivity influenced by summer cover crop and nitrogen fertilization

Cover crops and nitrogen (N) fertilization may affect soil mineral N, N uptake, and N productivity (grain yield per unit plant available N) in wheat (Triticum aestivum L.). We evaluated the effect of summer cover crops and N fertilization rates on soil mineral N and winter wheat grain yield, N uptake, and N productivity in the Loess Plateau of China. We measured cover crop aboveground biomass N, soil mineral N (0-20 cm), and winter wheat yield, N uptake, and N productivity for four seasons from 2017-2018 to 2020-2021. Cover crops were soybean (Glycine max L., SB), sudangrass (Sorghum sudanense [Piper] Stapf, SG], soybean and sudangrass mixture (SS), and no cover crop (CK) and N fertilization rates were 0, 60, and 120 kg N ha−1 applied to wheat. The SB increased cover crop biomass N and soil mineral N by 15 to 65%, but SS increased winter wheat yield, protein concentration, and N uptake by 25 to 55% compared to SG and CK at most N fertilization rates and years. Nitrogen fertilization rate had a variable effect on cover crop and wheat parameters. Wheat N productivity was 55 to 85% greater with CK than cover crops, but decreased with increased N fertilization rates. The N fertilizer equivalence of cover crops for wheat yield and N uptake ranged from 31 kg N ha−1 for SG to 150 kg N ha−1 for SS. Overall, SS with 60 kg N ha−1 can sustain winter wheat N uptake and N productivity compared to SG or CK with or without N fertilization.

Evaluation of Machine Learning Regression Techniques for Estimating Winter Wheat Biomass Using Biophysical, Biochemical, and UAV Multispectral Data

Crop above-ground biomass (AGB) estimation is a critical practice in precision agriculture (PA) and is vital for monitoring crop health and predicting yields. Accurate AGB estimation allows farmers to take timely actions to maximize yields within a given growth season. The objective of this study is to use unmanned aerial vehicle (UAV) multispectral imagery, along with derived vegetation indices (VI), plant height, leaf area index (LAI), and plant nutrient content ratios, to predict the dry AGB (g/m2) of a winter wheat field in southwestern Ontario, Canada. This study assessed the effectiveness of Random Forest (RF) and Support Vector Regression (SVR) models in predicting dry ABG from 42 variables. The RF models consistently outperformed the SVR models, with the top-performing RF model utilizing 20 selected variables based on their contribution to increasing node purity in the decision trees. This model achieved an R2 of 0.81 and a root mean square error (RMSE) of 149.95 g/m2. Notably, the variables in the top-performing model included a combination of MicaSense bands, VIs, nutrient content levels, nutrient content ratios, and plant height. This model significantly outperformed all other RF and SVR models in this study that relied solely on UAV multispectral data or plant leaf nutrient content. The insights gained from this model can enhance the estimation and management of wheat AGB, leading to more effective crop yield predictions and management.

Crop aboveground biomass monitoring model based on UAV spectral index reconstruction and Bayesian model averaging: A case study of film-mulched wheat and maize

Aboveground biomass (AGB) directly reflects crop carbon fixation capacity. Utilizing unmanned aerial vehicle (UAV) remote sensing for high-throughput AGB monitoring offers benefits to agricultural management and food production. This study aimed to develop a method for monitoring AGB in film-mulched crops using UAV multispectral data. A three-year field experiment was conducted using wheat (Exps. 1 and 2 in 2021–2022, and Exps. 5 and 6 in 2022–2023) and maize (Exps. 3 and 4) with varying cropping patterns. Spectral indices (SIs) of different feature reflectances (e.g., vegetation, soil, and mulch pixels) were reconstructed using least absolute shrinkage and selection operator methods. The Bayesian model averaging (BMA) method integrated seven independent machine learning algorithms to construct a Bayesian ensemble model (BEM). The results demonstrated that spectral index reconstruction (SIR) enhanced the correlation between SIs and AGB, while BMA reduced model uncertainty and improved AGB prediction accuracy. The BEM model, based on Exps. 1 and 2 datasets, achieved determination coefficients of 0.78–0.84 and root mean square error (RMSE) of 0.81–0.89 t ha−1. Model validation across years and crops demonstrated the generalization of the SIR + BEM method for AGB estimation. The RMSEs of the model inter-annual transport predictions were 0.83 and 0.93 t ha−1 in Exps. 5 and 6, respectively, and RMSE of the predictions between crops was 1.43 t ha−1 for the maize dataset (Exps. 3 + 4). The approach proposed in this study has great potential for monitoring growth status of film-mulched crops at the farm scale and providing guidance for precision agriculture management.

Crop‐ and weather‐dependent yield and wind erosion benefits from a conservation practices system

AbstractWind erosion and variable weather challenge crop production in the northern Great Plains. Management that increases residue cover might mitigate wind erosion during the cash crop growing season. We evaluated horizontal sediment flux (modified Wilson and Cooke samplers) and cash crop yield across a single rotation of corn (Zea mays L.)–soybean (Glycine max (L.) Merr.)–spring wheat (Triticum aestivum L.) in paired fields with contrasting management. One field included cover crops and retained spring wheat straw (aspirational), while the other excluded both conservation practices over the 3‐year rotation (business‐as‐usual). Horizontal sediment flux rapidly decreased with days after cash crop planting (increasing crop canopy), regardless of management treatment. In 2 years (2020 corn and 2022 spring wheat), there was greater horizontal sediment flux, lower cash crop grain yield, and lower cash crop aboveground biomass in the aspirational versus business‐as‐usual field. In 2021 soybean, there was lower horizontal sediment flux, greater cash crop yield, and greater cash crop aboveground biomass in the aspirational versus business‐as‐usual field. Higher yield and lower horizontal sediment flux responses corresponded with the management treatment that produced the higher cash crop aboveground biomass. Additionally, our short‐term study indicated that in drought years, cover crops worsened the adverse effects of abnormally low precipitation on yield and biomass of 2020 corn but not 2021 soybean.

The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection.

Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However, achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study, the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province, Thailand. Specifically, in order to explore, estimate, and map sugarcane AGB and carbon stock for the 2018 and 2021 years, ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently, optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically, the resulting AGB maps displayed noteworthy accuracy, with the coefficient of determination (R2) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t/ha for the years 2018 and 2021, respectively. In addition, mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally, it was shown how these highly accurate maps can support, as valuable tools, sustainable agricultural practices, government policy, and decision-making processes.

Catch and Cover Crops’ Use in the Energy Sector via Conversion into Biogas—Potential Benefits and Disadvantages

The development of civilization is related to an increase in energy demand, while its production is still based mainly on fossil fuels. The release of carbon into the environment, which disturbs the balance of the global system, is the consequence of using these fuels. One possible way to reduce the carbon footprint of the energy sector is the widespread use of cover crops’ biomass for energy production. The aim of this paper is to critically review the knowledge on the dissemination of catch and cover crops’ cultivation in different regions of the world, and the yield, chemical composition and biomethane potential of their biomass. Additionally, the environmental benefits, as well as the challenges and opportunities associated with this biomass use in the energy sector, are considered. The review showed that the aboveground biomass of cover and catch crops is a valuable source for the production of bioenergy in biogas plants. However, the key role of these crops is to prevent soil degradation. Therefore, changes in biomass target use must be preceded by a multi-aspect analysis that allows their impact on the environment to be assessed.

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models.

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

Net global warming potential index rather than soil carbon stock change could provide better understanding of the carbon balance in soil systems.

This study was conducted to determine the soil organic carbon (SOC) stock change factor for green manure crops that was developed by the Intergovernmental Panel on Climate Change (IPCC) Tier 2 method and compare this with the net global warming potential (GWP) index that is used to evaluate the contribution of green manuring to global warming. Four treatments were barley (Hordeum vulgare L.; B), hairy vetch (Vicia villosa R.; HV), a barley/hairy vetch mixture (BHV) and a conventional treatment (C). The aboveground biomass of green manure crops was incorporated into the soil on 25 May 2018, 26 April 2019, 29 April 2020, 30 April 2021 and 2 May 2022. Maize (Zea mays L.) was transplanted as the subsequent crop after the incorporation of green manures. SOC stock decreased with green manures, even though carbon input with green manures, including B, HV and BHV, was greater than that with C. The mean value of the SOC stock change factor for green manure crops, including B, HV and BHV was 0.627 and was significantly lower than that of the C. However, the net GWP also decreased with the incorporation of green manure crops, and the mean value of the relative net GWP index across B, HV and BHV was 0.853. These conflicting results were caused by different estimation methods between annual SOC change (△SOC) and net GWP. The estimation of SOC stock change using △SOC suggested by the IPCC method may overestimate the contribution of green manure crops to global warming. The net GWP method with comprehensive input and output of carbon in the soil system could provide a better understanding of the carbon balance in soil systems. In the current study, the comparison of △SOC and net GWP was conducted for at one site of upland soil for 5years. Therefore, further research on estimating the effect of green manure crops on net GWP in various types of soil for longer years should be conducted.

Modelling crop yield in a wheat–soybean relay intercropping system: A simple routine in capturing competition for light

Moving from sole cropping to intercropping is a transformative change in agriculture, contributing to several ecosystem services. However, modelling intercropping is challenging due to intensive parameterisation, complex calibration, and experiment scarcity. To facilitate future understanding, design and adaptation of intercropping, it is therefore necessary to develop simple modelling routines capable of simulating essential features. In this paper, we integrated a light competition module requiring four parameters into MONICA, a generic agroecosystem model, with the goal of simulating a wheat–soybean relay-row intercropping system. We tested three calibration approaches using data from two years of field experiments located in Müncheberg, Germany: sole cropping-based calibration, intercropping-based calibration and a default calibration method that incorporates both systems. Under both irrigated and rainfed conditions, MONICA successfully reproduced the aboveground biomass and yield of sole crops from field experiments, with RMSEA ranging from 0.64 t ha−1 to 2.74 t ha−1 and RMSEY ranging from 0.003 t ha−1 to 0.47 t ha−1. By taking light competition into account, the modified MONICA was able to simulate interactive performance in relay-row intercropping. Generally, MONICA overestimated the aboveground biomass and yield across the three calibration strategies, and simulations for wheat were more accurate than those for soybean. However, a comparison among the calibration strategies revealed that the intercropping-based strategy outperformed the others. It significantly improved the model efficiency for soybean yield in intercropping, increasing the Index of Agreement from 0.27 to 0.73, and it decreased the Mean Bias Error for yield by up to 76%. Our results demonstrate the feasibility of using a model that is simple in both calibration and inputs, yet detailed enough to simulate the complex aboveground light competition of intercropping. Additionally, they underscore the significance of cropping system specific calibration, highlighting the importance of calibrating crop performance specifically for intercropping in order to capture genotype-by-environment interactions.

Implementation of a dynamic specific leaf area (SLA) into a land surface model (LSM) incorporated crop-growth model

Croplands exert a strong influence on the land–atmosphere coupling and the surface exchanges of heat, water vapor, and momentum. The current trend is incorporating crop models into process-based land surface models (LSMs) to capture photosynthesis, crop growth process, and yield process. However, the models still have shortcomings, such as the assumption of constant SLA tends to ignore specific leaf area (SLA) changes caused by the accumulation and transport of photosynthetic products between organs. In this study, a dynamic SLA, which is specified for each crop, was implemented in the Noah-Multiparameterization Land Surface Model (Noah-MP-Crop), a coupled-crop-land-surface model, with the aim of improving the accuracy of estimated leaf area index (LAI) and above-ground biomass (leaf, stem and grain yield) of crops (maize, rice, soybean and winter wheat) in China. By including the dynamic SLA, the new model (Noah-MP-Cropsla) is more than qualified for capturing the measured variability in LAI and crops’ above-ground biomass, with the mean dim (Willmott’s index of agreement for the dynamic SLA scheme) of the four crops were all above 0.7. A remarkable improvement in the simulations of crop LAI and above-ground biomass were found during the crop’s jointing stage as indicated by the mean relative change rate of RMSE (RMSE% > 50%). Excluding the dynamic process of SLA will result in higher biases in the grain yield for most of the study sites. the improvement degree of crop yield was related to the improvement degree of LAI at the jointing stage (heading stage) of maize, rice and soybean (winter wheat). With the improved SLA process, the Noah-MP-Cropsla have a solid ability to capture the spatial pattern of crop yields, the R2 of the simulated and census data of the four major crop yield in China were all above 0.5.

Improved quantification of cover crop biomass and ecosystem services through remote sensing-based model–data fusion

Cover crops have long been seen as an effective management practice to increase soil organic carbon (SOC) and reduce nitrogen (N) leaching. However, there are large uncertainties in quantifying these ecosystem services using either observation (e.g. field measurement, remote sensing data) or process-based modeling. In this study, we developed and implemented a model–data fusion (MDF) framework to improve the quantification of cover crop benefits in SOC accrual and N retention in central Illinois by integrating process-based modeling and remotely-sensed observations. Specifically, we first constrained and validated the process-based agroecosystem model, ecosys, using observations of cover crop aboveground biomass derived from satellite-based spectral signals, which is highly consistent with field measurements. Then, we compared the simulated cover crop benefits in SOC accrual and N leaching reduction with and without the constraints of remotely-sensed cover crop aboveground biomass. When benchmarked with remote sensing-based observations, the constrained simulations all show significant improvements in quantifying cover crop aboveground biomass C compared with the unconstrained ones, with R 2 increasing from 0.60 to 0.87, and root mean square error (RMSE) and absolute bias decreasing by 64% and 97%, respectively. On all study sites, the constrained simulations of aboveground biomass C and N at termination are 29% and 35% lower than the unconstrained ones on average. Correspondingly, the averages of simulated SOC accrual and N retention net benefits are 31% and 23% lower than the unconstrained simulations, respectively. Our results show that the MDF framework with remotely-sensed biomass constraints effectively reduced the uncertainties in cover crop biomass simulations, which further constrained the quantification of cover crop-induced ecosystem services in increasing SOC and reducing N leaching.

Plant and soil N of different winter cover crops as green manure for subsequent organic white cabbage

Leguminous cover crops used as green manures can reduce fertilizer inputs by supplying nitrogen (N) via mineralization of incorporated N-rich biomass derived from biological N2 fixation. In a multi-year trial at three locations in Germany, the effects of leguminous, non-leguminous and mixed green manure crops on the yield of the subsequent cash crop white cabbage (Brassica oleracea convar. capitata var. alba) were investigated. The winter cover crop treatments were forage rye (Secale cereale L.), a mixture of forage rye with winter Hungarian vetch (Vicia pannonica Crantz), sole-cropped winter Hungarian vetch, winter pea (Pisum sativum L.), and winter faba bean (Vicia faba L.) with bare soil as a control. Sole-cropped legumes showed higher marketable cabbage head yields (head weight > 1.0 kg) compared to the other cover crop treatments, with 25.5, 25.9 and 28.1 Mg ha− 1 for vetch, pea and faba bean, respectively. The aboveground biomass of the legume winter cover crop treatments had higher N offtakes with 185, 177 and 159 kg N ha− 1 for vetch, pea and faba bean, respectively, with significantly lower carbon (C)/N ratios compared to rye and rye with vetch. The constant C/N ratio of the aboveground biomass of leguminous cover crops throughout the growing period indicates that the optimum incorporation date to achieve high N mineralization rates is less time dependent in leguminous compared to non-leguminous cover crops. The results of the present study show that leguminous winter cover crops do not reduce the soil N availability for a succeeding high N demanding cabbage crop resulting in yields comparable to agricultural practice without winter cover crops.

Dynamics of yield formation of green and dry aboveground biomass of non-traditional fodder crops in the Saratov Right Bank

The article deals with the problems of fodder production development in the Saratov region. In this regard, we paid great attention to annual fodder crops using the mogar as an example. The features of the formation of green and dry aboveground mogar biomass were described. Conducted research on the effect of seeding rate and sowing method on field germination and yield of mogar. We assessed the economic efficiency of mogar cultivation in the Saratov region. They recommended the optimal seeding rate and sowing method to obtain the maximum yield of mogar green mass.

A grass–legume cover crop maintains nitrogen inputs and nitrous oxide fluxes from an organic agroecosystem

AbstractLegume cover crops are central to an ecological nutrient management approach that can reduce nitrogen (N) losses from agriculture. Diversifying cropping systems with a legume–grass cover crop mixture could further reduce N losses by increasing soil N assimilation and synchronizing N mineralization with N uptake by the following crop. We established four winter cover crop treatments (crimson clover, cereal rye, clover–rye mixture, and weedy fallow control) in an organic grain agroecosystem that had been managed for 30 years with a legume cover crop as the only external N source. We hypothesized that the legume–grass mixture would provide similar inputs of biologically fixed N2 compared with the sole legume, while reducing N2O emissions during decomposition following tillage. We measured cover crop aboveground biomass C:N and clover N2 fixation, soil inorganic N and N2O fluxes throughout the corn growing season following cover crop tillage, and corn N assimilation at harvest. Even with a reduced clover seeding rate in mixture, the clover and mixture treatments had similar fixed N inputs, litter N and C:N, and no differences in cumulative N2O emissions. During the first peak flux, N2O emissions were 2–5 times higher in clover and mixture relative to rye and fallow, with no differences between clover and mixture. There were no treatment differences at the second N2O peak, which followed the first major rain event. We contextualized these findings by calculating a 6‐year partial N mass balance for this agroecosystem, which was slightly negative (−6.8 ± 0.8 kg N ha−1 year−1) when accounting for historical mean annual N2O emissions and nitrate leaching. Overall, N inputs and harvested N exports were approximately in balance for this legume‐based crop rotation, suggesting that the legacy of ecological nutrient management has promoted efficient N cycling. However, results from our field experiment indicate that short‐term N2O flux rates following cover crop incorporation can be high even for a legume–grass mixture. Additional strategies to reduce soil disturbance are therefore needed to further tighten N cycling in organic grain agroecosystems.

Estimating vertically growing crop above-ground biomass based on UAV remote sensing

The accurate estimation of crop above-ground biomass (AGB) can assist in crop growth monitoring and grain yield prediction. Remote sensing has been widely used for AGB estimation at regional and local scales in recent years. However, optical remote sensing spectral indices (SIs) become saturated at medium-to-high crop covers. The combined use of remote sensing techniques and statistical regression models is not based on an understanding of how crop leaves and vertical organs contribute to the crop AGB. This causes difficulties in measuring the biomass stored in vertical organs (e.g., plant stem, wheat-spike, maize-tassel; abbreviated as AGBv) using optical remote sensing. This study aims to develop an unmanned aerial vehicle (UAV)-based vertically growing crop AGB (VGC-AGB) model. We defined Csm (g/m) to describe the crop stem and reproductive organs’ average dry mass content. This was done to improve the estimation of AGBv. The crop leaf area index (LAI, m2/m2), leaf dry matter content (Cm, g/m2), height (Ch, m), and density (Cd, m−2) were used in the VGC-AGB. The VGC-AGB calculated crop leaf AGB (AGBl) using LAI × Cm (g/m2) and AGBv using Cd × Ch × Csm (g/m2). The proposed VGC-AGB (AGB = LAI × Cm + Cd × Ch × Csm) was verified using field and UAV-based hyperspectral datasets of winter-wheat and summer-maize at three growth stages. Our results indicate that UAV-based VGC-AGB (R2 = 0.92–0.93, RMSE = 68.82–75.15 g/m2) is superior to the statistical regression model that is based on remote sensing SIs and CSMs (R2 = 0.77, RMSE = 134.94 g/m2). The results indicate that the UAV-based VGC-AGB supports the analysis of crop photosynthetic product transfers and high-performance UAV-based high-performance non-destructive AGB monitoring.

Weed control under increasing cover crop diversity in tropical summer and winter

Description of the subject. Weed pressure is a main biotic constraint in tropical agriculture. Cover crop mixtures have increased in popularity to limit weed growth through competition for resources, but the relationship between cover crop diversity and weed suppression is still under debate. Objectives. This study aimed to assess the impact of increasing cover crop diversity (one to four species) on weed control during two growing seasons (tropical summer and winter) in Reunion Island. Method. Weed control was expressed regarding ground cover by weeds and weed aboveground dry mass in the mixtures during four months of growth and its response to cover crop traits was tested using structural equation models. Results. While cover crops reduced weed ground cover and dry mass by 60% and 68% on average in summer and winter, respectively, a higher number of cover crop species within a mixture did not increase mean weed control. Nonetheless, weed control was influenced by the mixture composition and improved when including Guizotia abyssinica. Additionally, cover crop traits explaining weed control differed between growing seasons. In summer, weed control was mainly explained by the final cover crop aboveground biomass and leaf area (depletion strategy). In contrast, weed control was mainly explained by the cover crop rate of increase in ground cover (obstruction strategy) in winter. Conclusions. Using traits to characterize cover crop mixture enables us to identify mixtures of species and traits adapted to different growing conditions. Our study suggests that particular attention on species identity rather than diversity should be paid in mixture to improve weed control in tropical conditions.

Disentangling the effect of nitrogen input and weed control on crop–weed competition suggests a potential agronomic trap in conventional farming

Weeds are commonly assumed to induce yield loss because of resource competition. However, there is growing empirical evidence that the picture is much more complex, because fertilizer inputs, by modifying the level of resources and weeding by removing some weed species, affect the outcome of crop-weed interaction. We assess how two important crop production inputs - nitrogen (N) fertilization and weed-control - affect a fundamental non-chemical means of weed management, the outcome of crop-weed competition, in real field conditions in 56 winter cereal fields, of which 23 were organically farmed. We used a factorial design with two levels (absence/presence) of nitrogen input and weed control inputs, but in our case, the “control” (i.e., presence level) for both practices was the usual practice of the farmer (which therefore varied). We found that crop aboveground biomass and grain yield were positively related to the amount of Ntotal (N soil plus N fertilizer), while weed species assemblages were negatively affected, showing lower species richness and weed abundance (i.e., number of plants). We also detected a contrast between farming systems: conventional fields (CF), managed with higher amount of total N and weed control, showed higher crop biomass and grain yield, and lower weed abundance compared to organically farmed fields (OF). Importantly, the findings showed that the outcome of the competition between crops and weeds was largely in favor of the crop plants in CF fields, even in the absence of weed control. In OF fields, the outcome of the competition between weeds and crop plants was still largely in favor of the crop, but at a lesser extent than in CF fields. The patterns were similar in unfertilized plots, though weed control in CF fields was more effective at low amounts of N, suggesting that more intense weed control is required in N-rich fields to maintain crop production. Overall, we argue that these results may underlie an agronomic trap: while an increased supply of nitrogen generally increases crop yield, it also benefits to weeds, requiring more efficient weed control.

Soil Properties and Maize Yield Improvement with Biochar-Enriched Poultry Litter-Based Fertilizer.

Conversion of poultry litter into fertilizer presents an environmentally friendly way for its disposal. The amendment of stabilizing sorption materials (e.g., biochar) to broiler chicken rearing seems promising, as it protects produced litter from nutrient losses and improves fertilizing efficacy. Thus, a pot experiment was carried out with maize and organic fertilizers produced from biochar-amended chicken bedding. The properties of three types of poultry-matured litter, amended with biochar at 0%, 10% and 20% dose, were analyzed. These matured litters were added to soil and physicochemical, biological properties and dry aboveground crop biomass yield were determined. Both biochar doses improved matured litter dry matter (+29%, +68% compared to unamended litter) and organic carbon (+5%, +9%). All three fertilizers significantly increased dry plant aboveground biomass yield (+3% and +42% compared to control litter-treated variant) and N-acetyl-β-D-glucosaminidase activity (+51%, +57%) compared to unamended control soil. The 20% biochar poultry-matured litter derived the highest dry plant aboveground biomass, highest respiration induced by D-glucose (+53%) and D-mannose (+35%, compared to control litter-treated variant), and decreased pH (-6% compared to unamended control). Biochar-derived modification of poultry litter maturation process led to organic fertilizer which enhanced degradation of soil organic matter in the subsequently amended soil. Furthermore, this type of fertilizer, compared to conventional unamended litter-based type, increased microbial activity, nutrient availability, and biomass yield of maize in selected biochar doses, even under conditions of significant soil acidification.