Готові списки джерел за темами / Crop and pasture biomass and bioproducts

Добірка наукової літератури з теми "Crop and pasture biomass and bioproducts"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Crop and pasture biomass and bioproducts"

Dos Reis, A. A., B. C. Silva, J. P. S. Werner, Y. F. Silva, J. V. Rocha, G. K. D. A. Figueiredo, J. F. G. Antunes, et al. "EXPLORING THE POTENTIAL OF HIGH-RESOLUTION PLANETSCOPE IMAGERY FOR PASTURE BIOMASS ESTIMATION IN AN INTEGRATED CROP–LIVESTOCK SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 419–24. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-419-2020.

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Abstract. Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely-sensed data. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May–August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g m−2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g m−2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.

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Rangwala, Murtaza, Jun Liu, Kulbir Singh Ahluwalia, Shayan Ghajar, Harnaik Singh Dhami, Benjamin F. Tracy, Pratap Tokekar, and Ryan K. Williams. "DeepPaSTL: Spatio-Temporal Deep Learning Methods for Predicting Long-Term Pasture Terrains Using Synthetic Datasets." Agronomy 11, no. 11 (November 5, 2021): 2245. http://dx.doi.org/10.3390/agronomy11112245.

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Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements.

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Sodré, Victoria, Nathália Vilela, Robson Tramontina, and Fabio Marcio Squina. "Microorganisms as bioabatement agents in biomass to bioproducts applications." Biomass and Bioenergy 151 (August 2021): 106161. http://dx.doi.org/10.1016/j.biombioe.2021.106161.

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Latif, Sajid, Saliya Gurusinghe, Paul A. Weston, William B. Brown, Jane C. Quinn, John W. Piltz, and Leslie A. Weston. "Performance and weed-suppressive potential of selected pasture legumes against annual weeds in south-eastern Australia." Crop and Pasture Science 70, no. 2 (2019): 147. http://dx.doi.org/10.1071/cp18458.

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Mixed farming systems have traditionally incorporated subterranean clover (Trifolium subterraneum L.) and lucerne (Medicago sativa L.) as key components of the pasture phase across south-eastern Australia. However, poor adaptation of subterranean clover to acidic soils, insufficient and inconsistent rainfall, high input costs, soil acidification and the emergence of herbicide-resistant weeds have reduced efficacy of some traditional clover species in recent years. To overcome these challenges, numerous novel pasture species have been selectively improved and released for establishment in Australia. Despite their suitability to Australian climate and soils, limited knowledge exists regarding their weed-suppressive ability in relation to establishment and regeneration. Field trials were therefore conducted over 3 years in New South Wales to evaluate the suppressive potential of selected pasture legume species and cultivars as monocultures and in mixed stands against dominant annual pasture weeds. Pasture and weed biomass varied significantly between pasture species when sown as monocultures, but mixtures of several species did not differ with regard to establishment and subsequent weed infestation. Arrowleaf clover (T. vesiculosum Savi.) and biserrula (Biserrula pelecinus L.) cv. Casbah showed improved stand establishment, with higher biomass and reduced weed infestation compared with other pasture species. Generally, weed suppression was positively correlated with pasture biomass; however, yellow serradella (Ornithopus compressus L.) cv. Santorini exhibited greater weed suppression than other pasture legumes while producing lower biomass, thereby suggesting a mechanism other than competition for resources affecting weed-suppressive ability. Over the period 2015–17, arrowleaf clover and biserrula cv. Casbah were generally the most consistent annual pasture legumes with respect to yearly regeneration and suppression of annual pasture weed species.

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Muniz, Luciano Cavalcante, Beata Emöke Madari, José Benedito de Freitas Trovo, Ilka South de Lima Cantanhêde, Pedro Luiz Oliveira de Almeida Machado, Tarcísio Cobucci, and Aldi Fernandes de Souza França. "Soil biological attributes in pastures of different ages in a crop-livestock integrated system." Pesquisa Agropecuária Brasileira 46, no. 10 (October 2011): 1262–68. http://dx.doi.org/10.1590/s0100-204x2011001000021.

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The objective of this work was to evaluate the effect of the pasture (Urochloa brizantha) component age on soil biological properties, in a crop-livestock integrated system. The experiment was carried out in a Brazilian savannah (Cerrado) area with 92 ha, divided into six pens of approximately 15 ha. Each pen represented a different stage of the pasture component: formation, P0; one year, P1; two years, P2; three years, P3; and final with 3.5 years, Pf. Samples were taken in the 0-10 cm soil depth. The soil biological parameters - microbial biomass carbon (MBC), microbial biomass respiration (C-CO2), metabolic quotient (qCO2), microbial quotient (q mic), and total organic carbon (TOC) - were evaluated and compared among different stages of the pasture, and between an adjacent area under native Cerrado and another area under degraded pasture (PCD). The MBC, q mic and TOC increased and qCO2 reduced under the different pasture stages. Compared to PCD, the pasture stages had higher MBC, q mic and TOC, and lower qCO2. The crop-livestock integrated system improved soil microbiological parameters and immobilized carbon in the soil in comparison to the degraded pasture.

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Harmoney, Keith R., Kenneth J. Moore, J. Ronald George, E. Charles Brummer, and James R. Russell. "Determination of Pasture Biomass Using Four Indirect Methods." Agronomy Journal 89, no. 4 (July 1997): 665–72. http://dx.doi.org/10.2134/agronj1997.00021962008900040020x.

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Punalekar, Suvarna M., Anna Thomson, Anne Verhoef, David J. Humphries, and Christopher K. Reynolds. "Assessing Suitability of Sentinel-2 Bands for Monitoring of Nutrient Concentration of Pastures with a Range of Species Compositions." Agronomy 11, no. 8 (August 20, 2021): 1661. http://dx.doi.org/10.3390/agronomy11081661.

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The accurate and timely assessment of pasture quantity and quality (i.e., nutritive characteristics) is vital for effective pasture management. Remotely sensed data can be used to predict pasture quantity and quality. This study investigated the ability of Sentinel-2 multispectral bands, convolved from proximal hyperspectral data, in predicting various pasture quality and quantity parameters. Field data (quantitative and spectral) were gathered for experimental plots representing four pasture types—perennial ryegrass monoculture and three mixtures of swards representing increasing species diversity. Spectral reflectance data at the canopy level were used to generate Sentinel-2 bands and calculate normalised difference indices with each possible band pair. The suitability of these indices for prediction of pasture parameters was assessed. Pasture quantity parameters (biomass and Leaf Area Index) had a stronger influence on overall reflectance than the quality parameters. Indices involving the 1610 nm band were optimal for acid detergent fibre, crude protein, organic matter and water-soluble carbohydrate concentration, while being less affected by biomass or LAI. The study emphasises the importance of accounting for the quantity parameters in the spectral data-based models for pasture quality predictions. These explorative findings inform the development of future pasture quantity and quality models, particularly focusing on diverse swards.

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Ward, P. R., R. A. Lawes, and D. Ferris. "Soil-water dynamics in a pasture-cropping system." Crop and Pasture Science 65, no. 10 (2014): 1016. http://dx.doi.org/10.1071/cp14046.

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Pasture cropping is a farming system in which annual crops are sown into established perennial pastures. It may provide environmental benefits such as increased groundcover and reduced deep drainage, while allowing traditional crop production in the Mediterranean-style climate of south-western Australia. In this research, we investigated deep drainage and the temporal patterns of water use by a subtropical perennial grass, annual crops, and a pasture-cropping system over a 4-year period. Both the pasture and pasture-cropped treatments reduced deep drainage significantly, by ~50 mm compared with the crop treatment. Competition between the pasture and crop components altered patterns of average daily water use, the pasture-cropped treatment having the highest water use for July, August and September. Consequently, water-use efficiency for grain production was lower in the pasture-cropped plots. This was offset by pasture production, so that over a full 12-month period, water-use efficiency for biomass production was generally greater for the pasture-cropped plots than for either the pasture or crop monocultures. Pasture cropping may be a viable way of generating sustainable economic returns from both crop and pasture production on sandy soils of south-western Australia.

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Cenciani, Karina, Marcio Rodrigues Lambais, Carlos Clemente Cerri, Lucas Carvalho Basílio de Azevedo, and Brigitte Josefine Feigl. "Bacteria diversity and microbial biomass in forest, pasture and fallow soils in the southwestern Amazon basin." Revista Brasileira de Ciência do Solo 33, no. 4 (August 2009): 907–16. http://dx.doi.org/10.1590/s0100-06832009000400015.

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It is well-known that Amazon tropical forest soils contain high microbial biodiversity. However, anthropogenic actions of slash and burn, mainly for pasture establishment, induce profound changes in the well-balanced biogeochemical cycles. After a few years the grass yield usually declines, the pasture is abandoned and is transformed into a secondary vegetation called "capoeira" or fallow. The aim of this study was to examine how the clearing of Amazon rainforest for pasture affects: (1) the diversity of the Bacteria domain evaluated by Polymerase Chain Reaction and Denaturing Gradient Gel Electrophoresis (PCR-DGGE), (2) microbial biomass and some soil chemical properties (pH, moisture, P, K, Ca, Mg, Al, H + Al, and BS), and (3) the influence of environmental variables on the genetic structure of bacterial community. In the pasture soil, total carbon (C) was between 30 to 42 % higher than in the fallow, and almost 47 % higher than in the forest soil over a year. The same pattern was observed for N. Microbial biomass in the pasture was about 38 and 26 % higher than at fallow and forest sites, respectively, in the rainy season. DGGE profiling revealed a lower number of bands per area in the dry season, but differences in the structure of bacterial communities among sites were better defined than in the wet season. The bacterial DNA fingerprints in the forest were stronger related to Al content and the Cmic:Ctot and Nmic:Ntot ratios. For pasture and fallow sites, the structure of the Bacteria domain was more associated with pH, sum of bases, moisture, total C and N and the microbial biomass. In general microbial biomass in the soils was influenced by total C and N, which were associated with the Bacteria domain, since the bacterial community is a component and active fraction of the microbial biomass. Results show that the genetic composition of bacterial communities in Amazonian soils changed along the sequence forest-pasture-fallow.

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Dos Reis, Aliny A., João P. S. Werner, Bruna C. Silva, Gleyce K. D. A. Figueiredo, João F. G. Antunes, Júlio C. D. M. Esquerdo, Alexandre C. Coutinho, Rubens A. C. Lamparelli, Jansle V. Rocha, and Paulo S. G. Magalhães. "Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery." Remote Sensing 12, no. 16 (August 6, 2020): 2534. http://dx.doi.org/10.3390/rs12162534.

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Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

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Частини книг з теми "Crop and pasture biomass and bioproducts"

Das, Pratyush Kumar, Bidyut Prava Das, and Patitapaban Dash. "Potentials of postharvest rice crop residues as a source of biofuel." In Refining Biomass Residues for Sustainable Energy and Bioproducts, 275–301. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-818996-2.00013-2.

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Тези доповідей конференцій з теми "Crop and pasture biomass and bioproducts"

Dos Reis, A. A., B. C. Silva, J. P. S. Werner, Y. F. Silva, J. V. Rocha, G. K. D. A. Figueiredo, J. F. G. Antunes, et al. "Exploring the Potential of High-Resolution Planetscope Imagery for Pasture Biomass Estimation in an Integrated Crop–Livestock System." In 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS). IEEE, 2020. http://dx.doi.org/10.1109/lagirs48042.2020.9165596.

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