Estimate Biomass Yield Research Articles

Evaluating UAV-Based Remote Sensing for Hay Yield Estimation.

(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel-1) and 50 m (35 mm pixel-1) heights. Eleven Vegetation Indices (VIs) and five texture features were calculated from the images to estimate biomass yield. Multivariate regression models (VIs and texture features vs. biomass) were evaluated. (3) Results: Model R2 values ranged from 0.31 to 0.68. (4) Conclusions: Despite strong correlations between standard VIs and biomass, challenges such as variable image resolution and clarity affected accuracy. Further research is needed before UAV-based yield estimation can provide accurate, high-resolution hay yield maps.

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

Assessment of climate change and its impact on hydrological regimes and biomass yield of a tropical river basin

Assessing climate change impact on hydrology and biomass yield of a tropical river basin is necessary for formulating a suitable coping strategy. A hydrological modelling framework was established for Subarnarekha river basin through calibration and validation of SWAT model with the observed river discharge at four gauging locations. The global sensitivity analysis was performed to establish the robustness and stability of the model to reproduce outputs resultant to various extremes of precipitation and other weather events. The 5-GCMs, namely HadGEM2-ES, MIROC-ESM, NCAR-CCSM4, CSIRO-MK3.6, and CESM1-CAM5 were analyzed and HadGEM2-ES was selected based on its performance with respect to ensemble of remaining 4 GCMs (p ≤ 0.05). The AR5-HadGEM2-ES monthly database (1 km2 resolutions) was used to generate bias factor for preparing daily climatic variables in the future projection which was employed as a forcing to simulate river discharge and other hydrological fluxes for the future climate scenario. Increase in precipitation from 14 to 36%, the runoff increased from 39.7 to 104.1%. When compared to the present period, an increase in monsoon runoff is expected 100 and 200% during 2030 and 2080, respectively. Higher degree of uncertainty in expected runoff volumes was expected in mid- and far future time period. Intra-RCP scenarios analyses indicated a significant surge in monsoon season runoff volume during 2070 and 2080 and insignificant surge during 2030 and 2050. As the basin constitute a rice ecosystem, the simulation approach involved a detailed accounting of the operational activities of a rice to realistically capture its manifestation in the annual accrual of actual evapotranspiration in the simulated hydrological fluxes. During monsoon season, potential evapotranspiration (PET) and actual evapotranspiration (AET) increased by 2.2–12.7% and 1.0–9.0%, respectively. Following simulation output, it was found that the basin was expected to suffer a crop biomass loss of 2 to 3% due to climate change. Simple yield response technique to estimate biomass yield losses was found to be promising alternatives to complex crop modeling studies.

Review on carbon dioxide fixation coupled with nutrients removal from wastewater by microalgae

The application of microalgae in carbon dioxide (CO2) fixation coupled with wastewater treatment has been regarded as a promising and cutting-edge technology in recent years, but it still faces some problems. The CO2 fixation capacity and nutrients removal capacity of microalgae should be considered simultaneously under various environmental conditions. Moreover, breeding microalgae using a photobioreactor is required and critical for CO2 fixation and wastewater treatment. In addition, the related influenced factors, mechanisms and kinetics of CO2 fixation and nutrients removal by microalgae remains complex and unclear. Thus, this work provides an updated review of the literature regarding the application of microalgae in the remediation of wastewaters from different sources coupled with CO2 fixation, discusses the main interactions between biological or environmental factors and how they can influence nutrient removal and CO2 fixation efficiencies, and further focuses on their mechanisms and kinetics. It is found that blue-green algae are most suitable to fix CO2 integrating treated wastewater, but more microalgal strains with excellent anti-fouling performance, tolerance to high concentrations of CO2 and NH3–N performance need to be screened. Additionally, wastewater and gas conditions during the cultivation process should be optimized in research and applications, and development of innovative photobioreactors with low cost is also required. Furthermore, modelling mainly focuses on estimating biomass yield, nutrient removal rates and CO2 fixation rates, while laboratory modelling results deviated from the natural modelling results. Finally, this paper also provides insight into what should be done in future work to support the development of CO2 fixation coupled with wastewater treatment by microalgae.

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%).

The application of NPK fertilizer boosts the nutrient uptake status and biomass production of Vernonia amygdalina

Abstract. Susanto D, Amirta R. 2020. The application of NPK fertilizer boosts the nutrient uptake status and biomass production of Vernonia amygdalina. Nusantara Bioscience 12: 114-119. Vernonia amygdalina Delile is a medicinal plant introduced in Indonesia. This study aims to determine the nutrients absorption status and growth of V. amygdalina plants planted with a spacing of 1x1 m. The study used a complete randomized block design with NPK compound fertilizer treatment with five doses of fertilizer namely 0 g (without fertilizer) as a control, 40 g, 80 g, 120 g, and 140 g, three blocks each group consisted of 10 plants so that in total there were 150 plants. The results showed that NPK fertilizer increased growth and the biomass of V. amygdalina. The best growth of V. amygdalina plant was achieved in plants applied with 160g per plant NPK fertilizer in which the average of stem diameter and plant height were 6.69 ± 0.930 cm and 611.67 ± 12.71 cm, while the estimated biomass yield was 407 tons.ha-1. The most nutritional elements accumulated in plant biomass was the calcium (1279.49 kg.ha-1), followed by magnesium (1167.0642 kg.ha-1), phosphorus (536.64 kg.ha-1), potassium (419.71 kg.ha-1) and nitrogen (134.4 kg.ha-1). We propose that these base nutrients are highly necessary for the cultivation of V. amygdalina as biomass feedstock or medicine plant.

13C Incorporation as a Tool to Estimate Biomass Yields in Thermophilic and Mesophilic Nitrifying Communities.

Current methods determining biomass yield require sophisticated sensors for in situ measurements or multiple steady-state reactor runs. Determining the yield of specific groups of organisms in mixed cultures in a fast and easy manner remains challenging. This study describes a fast method to estimate the maximum biomass yield (Ymax), based on 13C incorporation during activity measurements. It was applied to mixed cultures containing ammonia oxidizing bacteria (AOB) or archaea (AOA) and nitrite oxidizing bacteria (NOB), grown under mesophilic (15–28°C) and thermophilic (50°C) conditions. Using this method, no distinction could be made between AOB and AOA co-existing in a community. A slight overestimation of the nitrifier biomass due to 13C redirection via SMP to heterotrophs could occur, meaning that this method determines the carbon fixation activity of the autotrophic microorganisms rather than the actual nitrifier biomass yield. Thermophilic AOA yields exceeded mesophilic AOB yields (0.22 vs. 0.06–0.11 g VSS g-1 N), possibly linked to a more efficient pathway for CO2 incorporation. NOB thermophilically produced less biomass (0.025–0.028 vs. 0.048–0.051 g VSS g-1 N), conceivably attributed to higher maintenance requirement, rendering less energy available for biomass synthesis. Interestingly, thermophilic nitrification yield was higher than its mesophilic counterpart, due to the dominance of AOA over AOB at higher temperatures. An instant temperature increase impacted the mesophilic AOB yield, corroborating the effect of maintenance requirement on production capacity. Model simulations of two realistic nitrification/denitrification plants were robust toward changing nitrifier yield in predicting effluent ammonium concentrations, whereas sludge composition was impacted. Summarized, a fast, precise and easily executable method was developed determining Ymax of ammonia and nitrite oxidizers in mixed communities.

Using remote sensing to estimate forage biomass and nutrient contents at different growth stages

Lignocellulosic biomass is an important feedstock for the second generation bioenergy production. A sustainable supply of biomass feedstock having consistent composition is critical for a biorefinery. This requires a timely monitoring and estimation of the biomass yield and composition in the field. It is not clear if one can use the near infrared (NIR) vegetation canopy reflectance measured in the field and build a calibrate model to estimate biomass yield and nutrient contents (compositions) for the vegetation from vegetative growth through dormancy stages. In this study, the NIR canopy reflectance of a grass/legume mixture was measured in a field with a spectroradiometer with wavelength ranging from 400-2500 nm. The plants were then clipped, dried, and ground to measure the biomass yield, neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude protein (CP). The measurements were conducted at the boot, peak growth, and at dormancy stages. Partial least significant regression (PLSR) models were built using data from each individual growth stage as well as all stages combined. Except for the yield model at peak growth stage and the NDF and ADF models at dormancy stage, models developed from each of the individual stages generally estimated the yield, CP, NDF, and ADF poorly, with R2CV ranging between -0.31 and 0.42. When data from all three growth stages were included, the accuracy of all models was greatly improved, with R2CV ranging between 0.77 and 0.80. Furthermore, multiple linear regression (MLR) models developed with 7–9 most significant wavelengths selected from 400-2500 nm estimated the yield, ADF, NDF, and CP equally well compared with the PLSR models. The estimates from MLR model showed strong correlations between the measured and estimated values, with R2 of 0.72, 0.67, 0.78, and 0.66 for the yield, ADF, NDF, and CP, respectively. These results indicate that biomass feedstock yield and composition can be estimated by the in-situ NIR canopy reflectance using a multiple linear regression model with 7–9 wavelengths. Further calibration is needed to use the model to other geological locations.

Energycane growth dynamics and potential early harvest penalties along the Texas Gulf Coast

The seasonal dynamics of energycane biomass accumulation has major implications in designing optimized cellulosic biomass production and logistics systems. The objectives of this study are to: 1) quantify the growth and biomass dynamics of selected energycane genotypes along the Texas Gulf Coast and 2) estimate the yield penalty and nitrogen removal of different just-in-time harvesting schemes. Field experiments were conducted in three sites along the Texas Gulf Coast with four energycane genotypes. Biomass accumulation varied greatly among genotypes, but relative biomass growth, expressed as a proportion of each genotype's end of season biomass, was similar across years and genotypes for a particular site. On a calendar time basis, relative biomass growth curve became steeper at higher latitudes, indicating a shorter growing season, a narrower harvesting window, and a greater biomass yield penalty for early season harvest. Estimated biomass yield penalties for early August harvest were 60, 44, and 32% of the biomass at crop maturity for the northernmost, intermediate and southernmost site, respectively. Early harvesting of the same amount of biomass tends to remove more nitrogen due to higher organ nitrogen concentration. Combination of organ types harvested also greatly impacts nitrogen removal. Retaining leaf blade materials in the field would remove less nitrogen as compared to harvesting all organs, but will require greater harvesting area. Results from this study highlight the need for a systematic approach to integrate critical time- and site-dependent biomass growth characteristics of energycane in optimizing biomass production and supply logistics.

Experimental validation of in silico estimated biomass yields of Pseudomonas putida KT2440.

Pseudomonas putida is rapidly becoming a microbial cell platform for biotechnological applications. In order to understand genotype-phenotype relationships genome scale models represent helpful tools. However, the validation of in silico predictions of genome scale models is a task that is rarely performed. In this study the theoretical biomass yields of Pseudomonas putida KT2440 were estimated for 57 different carbon sources based on a genome scale stoichiometric model applying flux balance analysis. The batch growth of P. putida KT2440 with six individual carbon sources covering the range of maximal to minimal in silico biomass yields (acetate, glycerol, citrate, succinate, malate and methanol, respectively) was studied in a defined mineral medium in a fully controlled stirred-tank bioreactor on a 3 L scale. The highest growth rate of P. putida KT2440 was measured with succinate as carbon source (0.51 h-1 ). Among the 57 carbon sources tested, glycerol resulted in the highest estimated biomass yield (0.61 molCBiomass molC-1Glycerol ) which was experimentally confirmed. The comparison of experimental determined biomass yields with a modified version of the model iJP815 showed deviations of only up to 10%. The experimental data generated in this study can also be used in future studies to further improve the genome scale models of P. putida KT2440. Improved models will then help to gain deeper insights in genotype-phenotype relationships.

Optical sensors for variable rate nitrogen application in dairy pastures

Reducing the amount of nitrogen (N) fertiliser applied to dairy pastures down to agronomically optimised levels would have positive economic and environmental results. The ability of commercially available optical sensors to estimate biomass yield and foliar-N uptake in pastures was investigated. Vegetative indices (Simple Ratio, SR; Water Index, WI; and Normalised Difference Vegetation Index, NDVI) from two active optical reflectance sensors (N-Sensor, Yara; and Greenseeker, Trimble) were compared with manually measured biomass and N-uptake in above-ground foliage. There were three measurements over time, from pastures that had received different N fertiliser applications rates (0, 10, 20, 40 and 80 kg N/ha). It was found that the sensors were able to detect differences in biomass and foliar N-uptake following defoliation of grazed pastures. The tested optical sensors have the potential to inform a real-time variable rate fertiliser application system. Keywords: pasture, nitrogen, optical sensors

Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression

The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass “Cheyenne” (Sorghastrum nutans (L.) Nash) and 25 % big bluestem “Kaw” (Andropogon gerardii Vitman)] and switchgrass “Alamo”} and high biomass sorghum “Blade 5200” (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710–740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (r2 = 0.30 and RMSE = 1.6 Mg ha−1 in 2012 and r2 = 0.37 and RMSE = 4.0 Mg ha−1, but was strongly correlated for the high biomass sorghum in 2013 (r2 = 0.72 and RMSE = 4.6 Mg ha−1). Compared to the best narrowband VI, the RMSE of the PLSR model was 19–41 % lower for estimating N concentration and 4.2–100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.

Eddy covariance captures four-phase crassulacean acid metabolism (CAM) gas exchange signature in Agave.

Mass and energy fluxes were measured over a field of Agave tequilana in Mexico using eddy covariance (EC) methodology. Data were gathered over 252 d, including the transition from wet to dry periods. Net ecosystem exchanges (FN,EC ) displayed a crassulacean acid metabolism (CAM) rhythm that alternated from CO2 sink at night to CO2 source during the day, and partitioned canopy fluxes (FA,EC ) showed a characteristic four-phase CO2 exchange pattern. Results were cross-validated against diel changes in titratable acidity, leaf-unfurling rates, energy exchange fluxes and reported biomass yields. Projected carbon balance (g C m(-2) year(-1) , mean ± 95% confidence interval) indicated the site was a net sink of -333 ± 24, of which contributions from soil respiration were +692 ± 7, and FA,EC was -1025 ± 25. EC estimated biomass yield was 20.1 Mg (dry) ha(-1) year(-1) . Average integrated daily FA,EC was -234 ± 5 mmol CO2 m(-2) d(-1) and persisted almost unchanged after 70 d of drought conditions. Regression analyses were performed on the EC data to identify the best environmental predictors of FA . Results suggest that the carbon acquisition strategy of Agave offers productivity and drought resilience advantages over conventional semi-arid C3 and C4 bioenergy candidates.

Atmospheric CO2 fertilization effects on biomass yields of 10 crops in northern Germany

The quality and quantity of the influence that atmospheric CO2 has on crop growth is still a matter of debate. This study's aim is to estimate if CO2 will have an effect on biomass yields at all, to quantify and spatially locate the effects and to explore if an elevated photosynthesis rate or water-use-efficiency is predominantly responsible. This study uses a numerical carbon based crop model (BioSTAR) to estimate biomass yields within the administrative boundaries of Niedersachsen in Northern Germany. 10 crops are included (winter grains: wheat, barley, rye, triticale - early, medium, late maize variety - sunflower, sorghum, spring wheat), modeled annually for the entire 21st century on 91,014 separate sites. Modeling was conducted twice, once with an annually adapted CO2 concentration according to the SRES-A1B scenario and once with a fixed concentration of 390 ppm to separate the influence of CO2 from that of the other input variables. Rising CO2 concentrations will play a central role in keeping future yields of all crops above or around today's level. Differences in yields between modeling with fixed or adapted CO2 can be as high as 60 % towards the century's end. Generally yields will increase when CO2 rises and decline when it is kept constant. As C4-crops are equivalently affected it is presumed that an elevated efficiency in water use is the main responsible factor for all plants.

Influence of plant community composition on biomass production in planted grasslands.

United States energy policy mandates increased use of renewable fuels. Restoring grasslands could contribute to a portion of this requirement through biomass harvest for bioenergy use. We investigated which plant community characteristics are associated with differences in biomass yield from a range of realistic native prairie plantings (n = 11; i.e., conservation planting, restoration, and wildlife cover). Our primary goal was to understand whether patterns in plant community composition and the Floristic Quality Index (FQI) were related to productivity as evidenced by dormant season biomass yield. FQI is an objective measure of how closely a plant community represents that of a pre-European settlement community. Our research was conducted in planted fields of native tallgrass prairie species, and provided a gradient in floristic quality index, species richness, species diversity, and species evenness in south-central Wisconsin during 2008 and 2009. We used a network of 15 randomly located 1 m2 plots within each field to characterize the plant community and estimate biomass yield by clipping the plots at the end of each growing season. While plant community composition and diversity varied significantly by planting type, biomass yield did not vary significantly among planting types (ANOVA; P >0.05). Biomass yield was positively correlated with plant community evenness, richness, C4 grass cover, and floristic quality index, but negatively correlated with plant species diversity in our multi-season multiple linear mixed effects models. Concordantly, plots with biomass yield in the lowest quartile (biomass yield < 3500 kh/ha) had 8% lower plant community evenness and 9% lower FQI scores than those in the upper quartile (biomass yield > 5800 kh/ha). Our results suggest that promoting the establishment of fields with high species evenness and floristic quality may increase biomass yield, while simultaneously supporting biodiversity.

Performance and kinetic evaluation of a semi-continuously fed anaerobic digester treating food waste: Effect of trace elements on the digester recovery and stability

This study investigated the effects of trace elements (TEs: Fe, Co, and Ni) on the anaerobic digestion of food waste in laboratory scale semi-continuously fed anaerobic digesters. The digesters were operated for approximately 460d at organic loading rates (OLRs) ranging from 1.0 to 5.0gVSL−1d−1 at 37°C. Results showed that methane production was severely inhibited when volatile fatty acids (VFAs) concentration was 30000mgL−1 at OLR of 4.0gVSL−1d−1 without the addition of TEs. However, the inhibition was gradually decreased and the methane production increased from 0.13 up to 0.44Lg−1VSadded when the TEs was added. Fe was proved to be the essential element in maintaining the stability of the digester. The conversion coefficient of substrate into biogas (YS/G) was 1.00 and 1.26gVSremovedL−1 biogas with and without TEs addition, respectively. The estimated biomass yield with feeding TEs (0.06gVSSg−1CODremoved) was higher three times than that without TEs (0.02gVSSg−1CODremoved).

Estimating biomass on CRP pastureland: A comparison of remote sensing techniques

Biomass from land enrolled in the Conservation Reserve Program (CRP) is being considered as a biofuel feedstock source. A quick, accurate and nondestructive method to estimate biomass yield would be valuable for land managers to ensure sustainable production. The purpose of this study was to compare the ability of regression models to estimate biomass yields using data from satellite and ground based remote sensing platforms. Biomass yields and plant spectral responses were obtained at different phenological stages over two growing seasons (2011–2012) on an 8.1 ha CRP pasture in central Montana. Regression models were constructed using the normalized difference vegetation index (NDVI) and various band combinations from a hand held Crop Circle sensor and from Landsat satellite images. All models showed reasonable accuracy in estimating biomass, with a difference of <276 kg ha−1 or 8% of measured values. None of the models showed statistically significant differences (p > 0.05) between actual and estimated biomass. Results suggest that the usefulness of the spectral regions is a function of phenological growth stage. Red, red edge, and the near-infrared bands are more responsive at boot and peak growth stages while bands in the short-wave infrared increased the accuracy for the dormant stage biomass estimations. Land managers may construct spectral models to more effectively manage biomass resources.

Investigation on Regional Distribution of Potential Energy Production with Agricultural By-Products in Agricultural Sector

The objectives of this study were to estimate the potential biomass yield by using the biomass conversion index and evaluate the potential energy production by using the energy conversion index of biomass. Estimating the total biomass yield in Korea showed 9,646.3 thousand tons produced in 2012. Subsequent evaluation of the potential energy production using the estimated biomass yield in 2012 indicated that the calorific values were varied from 3,800 to 4,500 kcal <TEX>$kg^{-1}$</TEX> for crop- and from 4,100 to 4,300 kcal <TEX>$kg^{-1}$</TEX> for woody-based biomass, respectively. Among the examined biomass materials, the pruned branch of a nut tree appeared to be the greatest in bio-energy production showing 6,300 kcal <TEX>$kg^{-1}$</TEX> in calorific value. Total potential energy production from agricultural by-products was estimated approximately at 3,966,000 TOE. Among the agricultural by-products examined, rice straw showed the greatest energy production potential being at 2,321,000 TOE. Furthermore, it might contribute to establishing the countermeasures of biomass utility in agricultural sector based on regional distribution chart of the potential biomass and energy yields in Korea.

Kinetics evaluation of a semi-continuously fed anaerobic digester treating pig manure at two mesophilic temperatures

Anaerobic digestion of animal waste at a low range of mesophilic conditions has not been well described to date. In this study, laboratory-scale semi-continuously fed anaerobic digesters treating pig manure were operated at 28 and 38 °C with organic loading rates ranging from 1.3 to 4.3 g ODM L−1 d−1. The estimated biomass yield was higher at 28 °C (0.065 g VSS g−1 CODremoved) than at 38 °C (0.016 g VSS g−1 CODremoved). The resulting calculated biomass concentration range at 28 and 38 °C was 1.2–2.4 and 0.3–0.6g VSS L−1, respectively, which fitted well with a Michaelis–Menten type function. These VSS results are one or two orders of magnitude lower than previously reported for manure-fed digesters. Although maximum specific substrate utilisation rate at 38 °C is five-fold that at 28 °C, higher biomass yield in the digester at 28 °C seemed to compensate for the adverse effects of lower temperature on digester performance.

Variaciones diarias de la oferta forrajera, efecto sobre la producción y calidad de la leche

RESUMENObjetivo. Determinar el efecto de las variaciones de la oferta diaria de forraje sobre laproducción y calidad de la leche. Materiales y métodos. Determinando el área de ofertadiaria mediante un sistema de posicionamiento global y empleando la técnica de doblemuestreo para medir producción de biomasa previa y posterior al pastoreo, se estimó suconsumo diario por diferencia agronómica. Se estimaron variaciones dentro y entre fincaspara los parámetros evaluados. Se estableció el grado de asociación entre las variacionesdiarias en la oferta con el consumo, el volumen y calidad de la leche. Resultados. Losresultados evidenciaron asociación entre variaciones de oferta diaria y consumo de forraje(R2=0.65) y la variación en volumen diario de leche (R2=0.45). Las variaciones en grasano mostraron relación con cambios en la oferta (R2=0.0) y sus valores fueron asociadosa la relación forraje/suplemento (R2=0.52). Las variaciones en proteína tuvieron menorasociación con las variaciones en la oferta (R2=0.17) y mayor con el consumo de materiaseca (R2=0.34). Conclusiones. Los resultados sugieren que variaciones en la ofertaforrajera explican en mayor grado las variaciones en el consumo de materia seca con unefecto directo sobre el volumen de leche y en menor grado sobre las variaciones en sucomposición.