Investigation of Pressed Solid Biofuel Produced from Multi-Crop Biomass

The paper presents the preparation and use of pressed solid biofuel of multi-crop plants (fibrous hemp (Cannabis sativa L.), maize (Zea mays L.) and faba bean (Vicia faba L.)) as mono, binary and trinomial crops. The results of the investigation show that three main chemical elements (carbon, oxygen and hydrogen) accounted for 93.1 to 94.9% of the biomass pellet content. The moisture content varied from 3.9 to 8.8%, ash content from 4.5 to 6.8% and calorific value from 16.8 to 17.1 MJ·kg−1. It was found that the density (DM) of all variants of pellets was very similar; the faba bean biomass pellets had the highest density of 1195.8 kg·m−3 DM. The initial ash deformation temperature (DT) of burning biomass pellets was detected, which varied from 976 to 1322 °C. High potassium (K), calcium (Ca) and phosphorus (P) concentrations were found in all types of biomass ash. The quantities of heavy metals in pellet ash were not large and did not exceed the permissible values according to Lithuanian legislation. These chemical properties of multi-crop biomass ash allow them to be used in agriculture for plant fertilization.

Broadacre Mapping of Wheat Biomass Using Ground-Based LiDAR Technology

Crop biomass is an important attribute to consider in relation to site-specific nitrogen (N) management as critical N levels in plants vary depending on crop biomass. Whilst LiDAR technology has been used extensively in small plot-based phenomics studies, large-scale crop scanning has not yet been reported for cereal crops. A LiDAR sensing system was implemented to map a commercial 64-ha wheat paddock to assess the spatial variability of crop biomass. A proximal active reflectance sensor providing spectral indices and estimates of crop height was used as a comparison for the LiDAR system. Plant samples were collected at targeted locations across the field for the assessment of relationships between sensed and measured crop parameters. The correlation between crop biomass and LiDAR-derived crop height was 0.79, which is similar to results reported for plot scanning studies and greatly superior to results obtained for the spectral sensor tested. The LiDAR mapping showed significant crop biomass variability across the field, with estimated values ranging between 460 and 1900 kg ha−1. The results are encouraging for the use of LiDAR technology for large-scale operations to support site-specific management. To promote such an approach, we encourage the development of an automated, on-the-go data processing capability and dedicated commercial LiDAR systems for field operation.

Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.

Cover Crop Impacts on Water Dynamics and Yields in Dryland Wheat Systems on the Colorado Plateau

On the semiarid Colorado Plateau, dryland farmers are challenged by degraded soils and unreliable precipitation. While cover crops have been shown to support soil fertility, control erosion, and enhance in soil water capture, they also use limited soil water and, thus, may impact cash crop productivity in dryland systems. Most literature on cover crops comes from relatively humid climates, where yield penalties due to cover crops may be less pronounced. Two field trials were conducted in Southwestern Colorado to assess the short-term viability of cover crops in dryland systems in this region. The effect of cover crops on subsequent winter wheat (Triticum aestivum L.) yield ranged from a decrease of 78% to an increase of 13%, depending on the amount of cover crop biomass produced in the previous year. Cover crop biomass was inversely correlated with soil nitrate levels and soil water storage at wheat planting, which decreased by 0.39 mg kg−1 and 10 mm, respectively, per 1000 kg ha−1 of cover crop biomass produced. Less available soil water and immobilized N therefore appeared to contribute to wheat yield reductions. These impacts are particularly important for semiarid environments, where decomposition of residue is water-limited and soil water recharge depends on unpredictable precipitation patterns.