Machine learning based estimation of land productivity in the contiguous US using biophysical predictors

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works

Gaussian Processes for Machine Learning

Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works

A survey of transfer learning

Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260 · see citing works

Four billion people facing severe water scarcity

Mekonnen, M. M., & Hoekstra, A. Y. (2016) · Science Advances · doi:10.1126/sciadv.1500323 · Cited by 5,531 · see citing works

North American Regional Reanalysis

Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K. E., Shafran, P. C., Ebisuzaki, W., Jović, D., Woollen, J. S., Rogers, E., Berbery, E. H., et al. (2006) · Bulletin of the American Meteorological Society · doi:10.1175/bams-87-3-343 · Cited by 3,383 · see citing works

Deep learning in remote sensing applications: A meta-analysis and review

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456 · see citing works

Meeting the Global Food Demand of the Future by Engineering Crop Photosynthesis and Yield Potential

Long, S. P., Marshall‐Colón, A., & Zhu, X. (2015) · Cell · doi:10.1016/j.cell.2015.03.019 · Cited by 1,050 · see citing works

Random Forests for Global and Regional Crop Yield Predictions

Jeong, J. H., Resop, J. P., Mueller, N., Fleisher, D., Yun, K., Butler, E., Timlin, D., Shim, K., Gerber, J., Reddy, V., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156571 · Cited by 795 · see citing works

Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification

Yadav, S., & Shukla, S. (2016) · 2016 IEEE 6th International Conference on Advanced Computing (IACC) · doi:10.1109/iacc.2016.25 · Cited by 773 · see citing works

The global potential of bioenergy on abandoned agriculture lands

Campbell, J. E., Lobell, D. B., Genova, R. C., & Field, C. B. (2008) · Environmental Science & Technology · doi:10.1021/es800052w · Cited by 709 · see citing works

Theory, data, methods: developing spatially explicit economic models of land use change

Irwin, E. G., & Geoghegan, J. (2001) · Agriculture Ecosystems & Environment · doi:10.1016/s0167-8809(01)00200-6 · Cited by 656 · see citing works

Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3

Thornton, P., Thornton, M., Mayer, B., Wei, Y.-X., Devarakonda, R., Vose, R., & Cook, R. (2016) · Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics · doi:10.3334/ornldaac/1328 · Cited by 540 · see citing works

Machine learning methods for crop yield prediction and climate change impact assessment in agriculture

Crane‐Droesch, A. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aae159 · Cited by 517 · see citing works

Could Machine Learning Break the Convection Parameterization Deadlock?

Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. (2018) · Geophysical Research Letters · doi:10.1029/2018gl078202 · Cited by 497 · see citing works

Land Availability for Biofuel Production

Cai, X., Zhang, X., & Wang, D. (2011) · Environmental Science & Technology · doi:10.1021/es103338e · Cited by 444 · see citing works

Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife

Wager, S., Hastie, T. J., & Efron, B. (2013) · PubMed · doi:10.48550/arxiv.1311.4555 · Cited by 372 · see citing works

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

Fang, K., Shen, C., Kifer, D., & Yang, X. (2017) · Geophysical Research Letters · doi:10.1002/2017gl075619 · Cited by 368 · see citing works

Policy reform and agricultural land abandonment in the EU

Renwick, A., Jansson, T., Verburg, P. H., Revoredo‐Giha, C., Britz, W., Gocht, A., & McCracken, D. I. (2012) · Land Use Policy · doi:10.1016/j.landusepol.2012.04.005 · Cited by 364 · see citing works

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Han, J., Zhang, Z., Cao, J., Luo, Y., Zhang, L., Li, Z., & Zhang, J. (2020) · Remote Sensing · doi:10.3390/rs12020236 · Cited by 313 · see citing works

A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation

Camps‐Valls, G., Verrelst, J., Muñoz-Marı́, J., Laparra, V., Mateo, F., & Gomez-Dans, J. L. (2016) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2015.2510084 · Cited by 262 · see citing works

Environmental costs and benefits of growing<i>Miscanthus</i>for bioenergy in the<scp>UK</scp>

McCalmont, J. P., Hastings, A., McNamara, N. P., Richter, G. M., Robson, P., Donnison, I. S., & Clifton‐Brown, J. (2017) · GCB Bioenergy · doi:10.1111/gcbb.12294 · Cited by 244 · see citing works

What Drives Land-Use Change in the United States? A National Analysis of Landowner Decisions

Lubowski, R. N., Plantinga, A. J., & Stavins, R. N. (2008) · Land Economics · doi:10.3368/le.84.4.529 · Cited by 226 · see citing works

Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring

Campos‐Taberner, M., Garcı́a-Haro, F. J., Camps‐Valls, G., Grau-Muedra, G., Nutini, F., Crema, A., & Boschetti, M. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.10.009 · Cited by 217 · see citing works

A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression

Fang, D., Zhang, X., Yu, Q., Jin, T. C., & Luan, T. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.05.102 · Cited by 207 · see citing works

Terrestrial primary production for the conterminous United States derived from Landsat 30 m and MODIS 250 m

Robinson, N., Allred, B., Smith, W. K., Jones, M., Moreno‐Martínez, Á., Erickson, T. A., Naugle, D. E., & Running, S. W. (2018) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.74 · Cited by 195 · see citing works

Forcing, feedback and internal variability in global temperature trends

Marotzke, J., & Forster, P. M. D. F. (2015) · Nature · doi:10.1038/nature14117 · Cited by 194 · see citing works

What Drives Land-Use Change in the United States? A National Analysis of Landowner Decisions

Lubowski, R. N., Plantinga, A. J., & Stavins, R. N. (2007) · National Bureau of Economic Research · doi:10.3386/w13572 · Cited by 184 · see citing works

The Multi-Resolution Land Characteristics (MRLC) Consortium — 20 Years of Development and Integration of USA National Land Cover Data

Wickham, J., Homer, C. G., Vogelmann, J. E., McKerrow, A. J., Mueller, R., Herold, N., & Coulston, J. W. (2014) · Remote Sensing · doi:10.3390/rs6087424 · Cited by 181 · see citing works

Effect of Soil and Topographic Properties on Crop Yield in a North‐Central Corn–Soybean Cropping System

Jiang, P., & Thelen, K. D. (2004) · Agronomy Journal · doi:10.2134/agronj2004.0252 · Cited by 172 · see citing works

Climate change impacts on global agricultural land availability

Zhang, X., & Cai, X. (2011) · Environmental Research Letters · doi:10.1088/1748-9326/6/1/014014 · Cited by 168 · see citing works

Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest

Sadler, J. M., Goodall, J. L., Morsy, M. M., & Spencer, K. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.01.044 · Cited by 162 · see citing works

Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data

He, M., Kimball, J. S., Maneta, M., Maxwell, B. D., Moreno‐Martínez, Á., Beguerı́a, S., & Wu, X. (2018) · Remote Sensing · doi:10.3390/rs10030372 · Cited by 158 · see citing works

A novel framework to classify marginal land for sustainable biomass feedstock production

Gopalakrishnan, G., Cristina Negri, M., & Snyder, S. W. (2011) · Journal of Environmental Quality · doi:10.2134/jeq2010.0539 · Cited by 154 · see citing works

Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture

Brown, J. F., & Pervez, M. S. (2014) · Agricultural Systems · doi:10.1016/j.agsy.2014.01.004 · Cited by 154 · see citing works

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

Johnson, D. M. (2016) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2016.05.010 · Cited by 143 · see citing works

Overcome Support Vector Machine Diagnosis Overfitting

Han, H., & Jiang, X. (2014) · Cancer Informatics · doi:10.4137/cin.s13875 · Cited by 139 · see citing works

Where are the drought tolerant crops? An assessment of more than two decades of plant biotechnology effort in crop improvement

Nuccio, M. L., Paul, M. J., Bate, N. J., Cohn, J., & Cutler, S. R. (2018) · Plant Science · doi:10.1016/j.plantsci.2018.01.020 · Cited by 136 · see citing works

Marginal lands: the role of remote sensing in constructing landscapes for agrofuel development

Nalepa, R. A., & Bauer, D. M. (2012) · The Journal of Peasant Studies · doi:10.1080/03066150.2012.665890 · Cited by 123 · see citing works

meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced OverfittingPreprint

Sun, X., Ren, X., Ma, S., & Wang, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06197 · Cited by 121 · see citing works

Digital soil mapping of soil carbon at the farm scale: A spatial downscaling approach in consideration of measured and uncertain data

Malone, B., Styc, Q., Minasny, B., & McBratney, A. B. (2017) · Geoderma · doi:10.1016/j.geoderma.2016.12.008 · Cited by 120 · see citing works

Environmental impact assessment of energy crops cultivation in Europe

Fernando, A. L. A. D. C., Duarte, M. P., Almeida, J., Boléo, S., & Mendes, B. S. (2010) · Biofuels Bioproducts and Biorefining · doi:10.1002/bbb.249 · Cited by 116 · see citing works

An evaluation of the global potential of bioenergy production on degraded lands

Nijsen, M., Smeets, E., Stehfest, E., & van Vuuren, D. P. (2012) · GCB Bioenergy · doi:10.1111/j.1757-1707.2011.01121.x · Cited by 113 · see citing works

Standard errors for bagged and random forest estimators

Sexton, J., & Laake, P. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2008.08.007 · Cited by 102 · see citing works

Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm

Wang, H., Magagi, R., Goı̈ta, K., Trudel, M., McNairn, H. E., & Powers, J. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111234 · Cited by 102 · see citing works

An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty

Newlands, N. K., Zamar, D., Kouadio, L. A., Zhang, Y., Chipanshi, A. C., Potgieter, A. B., Touré, S., & Hill, H. S. J. (2014) · Frontiers in Environmental Science · doi:10.3389/fenvs.2014.00017 · Cited by 96 · see citing works

A Taxonomically Based Ordinal Estimate of Soil Productivity for Landscape-Scale Analyses

Schaetzl, R. J., Krist, F. J., & Miller, B. A. (2012) · Soil Science · doi:10.1097/ss.0b013e3182446c88 · Cited by 85 · see citing works

Measurement, scaling, and topographic analyses of spatial crop yield and soil water content

Green, T. R., & Erskine, R. H. (2004) · Hydrological Processes · doi:10.1002/hyp.1422 · Cited by 79 · see citing works

Mapping the Potential for Biofuel Production on Marginal Lands: Differences in Definitions, Data and Models across Scales

Lewis, S. M., & Kelly, M. (2014) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi3020430 · Cited by 78 · see citing works

Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems

Marshall, M., Tu, K. P., & Brown, J. F. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.08.001 · Cited by 78 · see citing works

Downgrading Recent Estimates of Land Available for Biofuel Production

Fritz, S., See, L., van der Velde, M., Nalepa, R. A., Perger, C., Schill, C., McCallum, I., Schepaschenko, D. G., Kraxner, F., Cai, X., et al. (2012) · Environmental Science & Technology · doi:10.1021/es303141h · Cited by 76 · see citing works

Marginal land suitability for switchgrass, Miscanthus and hybrid poplar in the Upper Mississippi River Basin (UMRB)

Feng, Q., Chaubey, I., Engel, B. A., Cibin, R., Sudheer, K. P., & Volenec, J. J. (2017) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2017.03.027 · Cited by 70 · see citing works

Are we approaching a water ceiling to maize yields in the United States?

DeLucia, E. H., Chen, S., Guan, K., Peng, B., Li, Y., Gomez‐Casanovas, N., Kantola, I. B., Bernacchi, C. J., Huang, Y., Long, S. P., et al. (2019) · Ecosphere · doi:10.1002/ecs2.2773 · Cited by 62 · see citing works

Sensitivity of DEM, slope, aspect and watershed attributes to LiDAR measurement uncertainty

Goulden, T., Hopkinson, C., Jamieson, R., & Sterling, S. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.03.005 · Cited by 61 · see citing works

Evaluating the Potential of Marginal Land for Cellulosic Feedstock Production and Carbon Sequestration in the United States

Emery, I., Mueller, S., Qin, Z., & Dunn, J. B. (2017) · Environmental Science &amp; Technology · doi:10.1021/acs.est.6b04189 · Cited by 53 · see citing works

Gross primary production responses to warming, elevated CO 2, and irrigation: quantifying the drivers of ecosystem physiology in a semiarid grassland

Ryan, E., Ogle, K., Peltier, D. M. P., Walker, A. P., Kauwe, M. G. D., Medlyn, B. E., Williams, D. G., Parton, W. J., Asao, S., Guenet, B., et al. (2016) · Global Change Biology · doi:10.1111/gcb.13602 · Cited by 51 · see citing works

Gaussian processes for machine learning

Canelles, G. M. (2017) · Cited by 47 · see citing works

Potential marginal land resources of cassava worldwide: A data-driven analysis

Jiang, D., Wang, Q., Ding, F., Fu, J., & Hao, M. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.01.024 · Cited by 37 · see citing works

Estimating maize and cotton yield in southeastern Turkey with integrated use of satellite images, meteorological data and digital photographs

Algancı, U., Mutlu Ozdogan, A., Sertel, E., & Örmeci, C. (2013) · Field Crops Research · doi:10.1016/j.fcr.2013.12.006 · Cited by 34 · see citing works

Miscanthus spatial location as seen by farmers: A machine learning approach to model real criteria

Rizzo, D., Martin, L., & Wohlfahrt, J. (2014) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2014.02.035 · Cited by 32 · see citing works

Computer‐aided prediction of toxicity with substructure pattern and random forest

Cao, D., Yang, Y., Zhao, J., Yan, J., Shao, L., Hu, Q., Xu, Q., & Liang, Y. (2012) · Journal of Chemometrics · doi:10.1002/cem.1416 · Cited by 30 · see citing works

Adopting bioenergy crops: Does farmers’ attitude toward loss matter?

Anand, M., Miao, R., & Khanna, M. (2019) · Agricultural Economics · doi:10.1111/agec.12501 · Cited by 27 · see citing works

Assessing Sustainable Bioenergy Feedstock Production Potential by Integrated Geospatial Analysis of Land Use and Land Quality

Wightman, J. L., Ahmed, Z., Volk, T. A., Castellano, P. J., Peters, C. J., DeGloria, S. D., Duxbury, J. M., & Woodbury, P. B. (2015) · BioEnergy Research · doi:10.1007/s12155-015-9618-x · Cited by 25 · see citing works

Simulated impact of the renewable fuels standard on US Conservation Reserve Program enrollment and conversion

Hellwinckel, C., Clark, C. M., Langholtz, M. H., & Eaton, L. (2015) · GCB Bioenergy · doi:10.1111/gcbb.12281 · Cited by 19 · see citing works

Some implications of biofuel development for engineering infrastructures in the United States

Ng, T. L., Cai, X., & Ouyang, Y. (2011) · Biofuels Bioproducts and Biorefining · doi:10.1002/bbb.309 · Cited by 16 · see citing works

Bioenergy and biodiversity: Intensified biomass extraction from hedges impairs habitat conditions for birds

Sauerbrei, R., Aue, B., Krippes, C., Diehl, E., & Wolters, V. (2016) · Journal of Environmental Management · doi:10.1016/j.jenvman.2016.11.052 · Cited by 11 · see citing works

Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production

Shelare, S. D., Belkhode, P. N., Nikam, K. C., Jathar, L. D., Shahapurkar, K., Soudagar, M. E. M., Veza, I., Khan, T. M. Y., Kalam, M. A., Nizami, A., et al. (2023) · Energy · doi:10.1016/j.energy.2023.128874 · Cited by 156 · see citing works

Concepts of agricultural marginal lands and their utilisation: a review

Csikós, N., & Tóth, G. (2023) · Agricultural Systems · doi:10.1016/j.agsy.2022.103560 · Cited by 135 · see citing works

Redefining marginal land for bioenergy crop production

Khanna, M., Chen, L., Basso, B., Cai, X., Field, J. L., Guan, K., Jiang, C., Lark, T. J., Richard, T. L., Spawn‐Lee, S. A., et al. (2021) · GCB Bioenergy · doi:10.1111/gcbb.12877 · Cited by 105 · see citing works

A Review on Machine Learning Application in Biodiesel Production Studies

Xing, Y., Zheng, Z., Sun, Y., & Alikhani, M. A. (2021) · International Journal of Chemical Engineering · doi:10.1155/2021/2154258 · Cited by 90 · see citing works

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Ahmad, I., Kano, M., Menezes, B. C., Cheema, I. I., Sana, A., Shahzad, J., Ullah, Z., Khan, M., & Habib, A. (2021) · Energies · doi:10.3390/en14165072 · Cited by 52 · see citing works

Using Machine Learning and Feature Selection for Alfalfa Yield Prediction

Whitmire, C., Vance, J. M., Rasheed, H. K., Missaoui, A., Rasheed, K., & Maier, F. W. (2021) · AI · doi:10.3390/ai2010006 · Cited by 47 · see citing works

Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States

Jiang, C., Guan, K., Khanna, M., Chen, L., & Peng, J. (2021) · Environmental Science &amp; Technology · doi:10.1021/acs.est.1c02236 · Cited by 43 · see citing works

Sustainable land use and viability of biojet fuels

Aragon, N. U., Parker, N., VanLoocke, A. D., Bagley, J. E., Wang, M., & Georgescu, M. (2022) · Nature Sustainability · doi:10.1038/s41893-022-00990-w · Cited by 34 · see citing works

Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

Kim, S., Seo, J., & Kim, S. (2024) · Energies · doi:10.3390/en17061316 · Cited by 21 · see citing works

Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning

Cacho, J., Feinstein, J., Zumpf, C. R., Hamada, Y., Lee, D. J., Namoi, N., Lee, D., Boersma, N. N., Heaton, E. A., Quinn, J. J., et al. (2023) · Energies · doi:10.3390/en16104168 · Cited by 20 · see citing works

Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms

Peng, W., & Sadaghiani, O. K. (2023) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2023.106884 · Cited by 19 · see citing works

Techno-economic assessment of bioenergy potential on marginal croplands in the U.S. southeast

Wu, J. S., Tseng, H.-K., & Liu, X. (2022) · Energy Policy · doi:10.1016/j.enpol.2022.113215 · Cited by 15 · see citing works

Farmers' heterogeneous perceptions of marginal land for biofuel crops in US Midwestern states considering biophysical and socioeconomic factors

Yang, P., Cai, X., & Khanna, M. (2021) · GCB Bioenergy · doi:10.1111/gcbb.12821 · Cited by 15 · see citing works

An agent-based modeling tool supporting bioenergy and bio-product community communication regarding cellulosic bioeconomy development

Yang, P., Cai, X., Hu, X., Zhao, Q., Lee, Y. S., Khanna, M., Cortes-Pena, Y. R., Guest, J. S., Kent, J., Hudiburg, T. W., et al. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112745 · Cited by 15 · see citing works

Addressing the Sustainable Aviation Fuel Grand Challenge: Economic and Environmental Insights from Feedstock Optimization

Limb, B. J., Smith, J. P., Simske, S., & Quinn, J. C. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c05842 · Cited by 12 · see citing works

Water Availability for Biorefineries in the Contiguous United States and the Implications for Bioenergy Production Distribution

Yang, P., Piao, X., & Cai, X. (2022) · Environmental Science & Technology · doi:10.1021/acs.est.1c07747 · Cited by 11 · see citing works

A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials

Peng, W., & Sadaghiani, O. K. (2023) · Energy Sources Part A Recovery Utilization and Environmental Effects · doi:10.1080/15567036.2023.2232322 · Cited by 13 · see citing works

Integrated Biorefinery for Valorization of Engineered Bioenergy Crops—A Review

Singh, R. D., & Singh, V. (2021) · Industrial Biotechnology · doi:10.1089/ind.2021.0020 · Cited by 10 · see citing works

Marginal agricultural land identification in the Lower Mississippi Alluvial Valley based on remote sensing and machine learning model

Tiwari, P., Poudel, K. P., Yang, J., da Silva, B. K., Yang, Y., & McConnell, M. D. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103568 · Cited by 9 · see citing works

Machine learning for sustainable reutilization of waste materials as energy sources – a comprehensive review

Peng, W., & Sadaghiani, O. K. (2023) · International Journal of Green Energy · doi:10.1080/15435075.2023.2255647 · Cited by 9 · see citing works

Policy-driven improvements in cultivated land productivity: Changed determinants in Huang-Huai-Hai Plain, China

Bai, X., Zhang, J., Lyu, Y., Fleskens, L., Cui, Z., Liu, X., Zeng, X., Ritsema, C. J., & Zhang, F. S. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.126460 · Cited by 8 · see citing works

Using Machine Learning to Identify the Potential Marginal Land Suitable for Giant Silvergrass (Miscanthus × giganteus)

Hao, M., Chen, S., Qian, Y., Jiang, D., & Ding, F. (2022) · Energies · doi:10.3390/en15020591 · Cited by 8 · see citing works

Soil Organic Carbon Sequestration

Lorenz, K. E., & Lal, R. (2022) · Springer eBooks · doi:10.1007/978-3-030-95193-1_3 · Cited by 8 · see citing works

Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

Yu, T., Pang, Y., Sun, R., & Niu, X. (2022) · IEEE Access · doi:10.1109/access.2022.3210218 · Cited by 7 · see citing works

Incentivizing Soil Organic Carbon Management in Terrestrial Biomes of the United States of America

Lorenz, K. E., & Lal, R. (2022) · Springer eBooks · doi:10.1007/978-3-030-95193-1_5 · Cited by 5 · see citing works

The role of social support on midwestern farmers’ willingness to grow perennial bioenergy crops

Yang, B., Yang, P., Golub, E., & Cai, X. (2023) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2023.106898 · Cited by 5 · see citing works

Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources

Zou, Y., Kattel, G., & Miao, L. (2024) · Remote Sensing · doi:10.3390/rs16040701 · Cited by 5 · see citing works

Decadal change in soil carbon and nitrogen with a <i>Miscanthus</i> × <i>giganteus</i> crop on abandoned agricultural land in southeast Ohio

Adu Fosu, S., & Davis, S. C. (2024) · GCB Bioenergy · doi:10.1111/gcbb.13171 · Cited by 4 · see citing works

Role of Machine Learning and Artificial Intelligence in Biofuel/Bioenergy Productions

Mansab, S., Nasreen, S., & Parveen, K. (2025) · Clean Energy Production Technologies · doi:10.1007/978-981-96-4636-4_13 · Cited by 4 · see citing works

Overview: Catalysts, Feedstocks in Biodiesel Production

Taft, C. A., & Canchaya, J. G. S. (2022) · Engineering materials · doi:10.1007/978-3-031-07622-0_13 · Cited by 3 · see citing works

A review on the application of machine learning in production of woody biomass from natural and planted forests

Peng, W., & Sadaghiani, O. K. (2023) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0149793 · Cited by 2 · see citing works

Coordination Analysis of Sustainable Agricultural Development in Northeast China from the Perspective of Spatiotemporal Relationships

Yang, G., Hua, Y., & Li, Q. (2023) · Sustainability · doi:10.3390/su152316354 · Cited by 2 · see citing works

Sustainable Development of Marginal Lands for Food Security: Advances, Ecological Constraints, and Remediation Strategies

Wu, C., & Han, Y. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c13562 · Cited by 1 · see citing works

Assessing the reproduction of land resources in the context of achieving food security

Golovin, A. A., Soklakov, A. A., & Spitsyna, A. O. (2022) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1010/1/012130 · Cited by 1 · see citing works

Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale

Zhu, Z., & Xie, Y. (2024) · Applied Sciences · doi:10.3390/app14073130 · Cited by 1 · see citing works

China’s bioenergy potential will be stable and decoupling from economy and population at national level: A study based on machine regression prediction model

Chen, Y., Huang, Z., Ma, C., Wang, Y., Chen, W., Zhang, Z., Tan, T., & Chen, Y. (2024) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2024.103927 · Cited by 1 · see citing works

Classification and Evaluation of Marginal Land for Potential Cultivation in Northwest China Based on Contiguity and Restrictive Factors

Zhang, A., Wang, S., Zhang, Z., Niu, J., Guo, M., Ye, H., Guo, X., Su, R., & Tang, H. (2024) · Agronomy · doi:10.3390/agronomy14102413 · Cited by 1 · see citing works

Predicting the Productive Potential of Land for Pastoral Sheep and Beef Farming in New ZealandPreprint

Harris, S. R., Cichota, R., Lilburne, L., & Fraser, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4827752

Responses of Hydrological Processes to Future Climate Variability and Agricultural Land Afforestation in a Humid River Basin

Wu, Q., Yang, J., Ouyang, Y., Siegert, C. M., Li, P., & Song, J. (2026) · Ecohydrology · doi:10.1002/eco.70276

Land productivity and land availability for growing bioenergy crop in the Contiguous US. Center for Advanced Bioenergy and Bioproducts Innovation (CABBI)

Yang, P., Zhao, Q., & Cai, X. (2019) · doi:10.13012/b2idb-4584681_v1

Machine learning based estimation of land productivity in the continental US using biophysical predictors

YANG, P. ‐., Zhao, Q., & Cai, X. (2019) · AGU Fall Meeting Abstracts

Assessing Marginal Land Availability Based on Land Use Change Information in the Contiguous United States

Jiang, C., Guan, K., Khanna, M., Chen, L., & Peng, J. (2021) · Environmental Science &amp; Technology · doi:10.1021/acs.est.1c02236 · Cited by 43 · see citing works

Farmers' heterogeneous perceptions of marginal land for biofuel crops in US Midwestern states considering biophysical and socioeconomic factors

Yang, P., Cai, X., & Khanna, M. (2021) · GCB Bioenergy · doi:10.1111/gcbb.12821 · Cited by 15 · see citing works

Economic Potential for Using Marginal Land for Bioenergy Crop Production in the US

Debnath, D., Chen, L., Khanna, M., Jiang, C., & Guan, K. (2019)