Spatially-explicit projection of future microbial protein from lignocellulosic waste

Aday, 2020, Impact of COVID-19 on the food supply chain, Food Qual. Saf., 4, 167, 10.1093/fqsafe/fyaa024 Ahamed, A.M.S., Mahmood, N.T., Hossain, N., Kabir, M.T., Das, K., Rahman, F., et al., editors. 2015. Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh. In: 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD). IEEE. Anselin, L., 2001. Spatial econometrics. A companion to theoretical econometrics. 310330. Asim, 2021, Production of Food-Grade Glucose from Rice and Wheat Residues Using a Biocompatible Ionic Liquid, ACS Sustain. Chem. Eng., 9, 8080, 10.1021/acssuschemeng.1c00022 Bank, T.W., 2018. World Bank Open Data. Bhatia, N., 2010. Survey of nearest neighbor techniques. arXiv preprint arXiv:10070085. Cameron, 1997, An R-squared measure of goodness of fit for some common nonlinear regression models, J. Econometr., 77, 329, 10.1016/S0304-4076(96)01818-0 Chen, L., 2021. Implementation of future microbial protein projection from lignocellulosic waste. GitHub. Available from: https://github.com/MGuo-Lab/Single-Cell-Protein. Crane-Droesch, 2018, Machine learning methods for crop yield prediction and climate change impact assessment in agriculture, Environ. Res. Lett., 13, 10.1088/1748-9326/aae159 Dahikar, 2014, Agricultural crop yield prediction using artificial neural network approach, Int. J. Innov. Res. Electr., Electron., Instrumentation Control Eng., 2, 683 Dimri, 2020, Time series analysis of climate variables using seasonal ARIMA approach, J. Earth Syst. Sci., 129, 1, 10.1007/s12040-020-01408-x Diniz-Filho, 2003, Spatial autocorrelation and red herrings in geographical ecology, Glob. Ecol. Biogeogr., 12, 53, 10.1046/j.1466-822X.2003.00322.x Dismuke, 2006, Ordinary least squares, Methods Des. Outcomes Res., 93, 93 Dubin, R., Fotheringham, A., Rogerson, P., 2009. Spatial weights. The Sage handbook of spatial analysis. 125–58. FAOSTAT [Internet], 2018. Available from: http://www.fao.org/faostat/en/#data. Galdi, 2018, Data mining: accuracy and error measures for classification and prediction, Encyclopedia Bioinform. Computat. Biol., 431 Henchion, 2017, Future protein supply and demand: strategies and factors influencing a sustainable equilibrium, Foods., 6, 53, 10.3390/foods6070053 Henley, 2010, The importance of dietary protein in human health: Combating protein deficiency in sub-Saharan Africa through transgenic biofortified sorghum, Adv. Food Nutr. Res., 60, 21, 10.1016/S1043-4526(10)60002-2 Hillmer, 1982, An ARIMA-model-based approach to seasonal adjustment, J. Am. Stat. Assoc., 77, 63, 10.1080/01621459.1982.10477767 Hjort, 2008, Effects of sample size on the accuracy of geomorphological models, Geomorphology, 102, 341, 10.1016/j.geomorph.2008.04.006 Huntington, 2020, Machine learning to predict biomass sorghum yields under future climate scenarios, Biofuels, Bioprod. Biorefin., 14, 566, 10.1002/bbb.2087 Ip, 1999, An investigation of stochastic analysis of flexible manufacturing systems simulation, Int. J. Adv. Manuf. Technol., 15, 244, 10.1007/s001700050063 Jacovides, 1995, Statistical procedures for the evaluation of evapotranspiration computing models, Agric. Water Manag., 27, 365, 10.1016/0378-3774(95)01152-9 Leng, 2020, Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models, Environ. Res. Lett., 15, 10.1088/1748-9326/ab7b24 Liakos, 2018, Machine learning in agriculture: A review, Sensors., 18, 2674, 10.3390/s18082674 Matsumura, 2015, Maize yield forecasting by linear regression and artificial neural networks in Jilin, China, J. Agric. Sci., 153, 399, 10.1017/S0021859614000392 Mehta, 2002, Rainfall variability analysis and its impact on crop productivity-A case study, Indian J. Agric. Res., 36, 29 Mishra, 2020, Power-to-protein: carbon fixation with renewable electric power to feed the world, Joule., 4, 1142, 10.1016/j.joule.2020.04.008 Monitor, G.C., 2018. Munson, M.A., Caruana, R., editors, 2009. On feature selection, bias-variance, and bagging. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer. Nasseri, 2011, Single cell protein: production and process, Am. J. Food Technol., 6, 103, 10.3923/ajft.2011.103.116 Panoutsou, C., Labalette, F., 2006. Cereals straw for bioenergy and competitive uses. In: Proceedings of the Cereals Straw Resources for Bioenergy in the European Union, Pamplona, Pamplona, 18–9. Patterson, P., Makus, L., Momont, P., Robertson, L., 1995. The availability, alternative uses and value of straw in Idaho. Final Report of the Project BDK251, Idaho Wheat Commission, College of Agriculture, University of Idaho. Piercy, E., Verstraete, W., Ellis, P., Rockström, J., Smith, P., Witard, O., Hogstrand C., Hallett, J., Knott, G., Karwati, A., Rosoarahona, H.F., Leslie, A., Guo, M. A sustainable solution for hunger pandemics? Pihlajaniemi, 2020, Comparison of pretreatments and cost-optimization of enzymatic hydrolysis for production of single cell protein from grass silage fibre, Bioresour. Technol. Reports, 9, 10.1016/j.biteb.2019.100357 Ramchoun, 2016, Multilayer Perceptron: Architecture Optimization and Training, Int. J. Interact Multim Artif Intell., 4, 26 Ramesh, 2015, Analysis of crop yield prediction using data mining techniques, Int. J. Res. Eng. Technol., 4, 47 Rand, 2003, Meta-analysis of nitrogen balance studies for estimating protein requirements in healthy adults, Am. J. Clin. Nutrit., 77, 109, 10.1093/ajcn/77.1.109 Ranstam, 2018, LASSO regression. Journal of British, Surgery., 105, 1348 Ritala, 2017, Single cell protein—state-of-the-art, industrial landscape and patents 2001–2016, Front. Microbiol., 8, 2009, 10.3389/fmicb.2017.02009 Scarlat, 2010, Assessment of the availability of agricultural crop residues in the European Union: potential and limitations for bioenergy use, Waste Manage., 30, 1889, 10.1016/j.wasman.2010.04.016 Shahhosseini, 2019, Maize yield and nitrate loss prediction with machine learning algorithms, Environ. Res. Lett., 14, 10.1088/1748-9326/ab5268 Upcraft, 2021, Protein from renewable resources: mycoprotein production from agricultural residues, Green Chem., 23, 5150, 10.1039/D1GC01021B van Dijk, 2021, A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050, Nature Food., 2, 494, 10.1038/s43016-021-00322-9 Vogel, 2019, The effects of climate extremes on global agricultural yields, Environ. Res. Lett., 14, 10.1088/1748-9326/ab154b Voutilainen, 2021, Economic comparison of food protein production with single-cell organisms from lignocellulose side-streams, Bioresour. Technol. Reports, 14, 10.1016/j.biteb.2021.100683 Woolston, 2020, Healthy people, healthy planet: the search for a sustainable global diet, Nature, 588, S54, 10.1038/d41586-020-03443-6 Worldometer. World Population Projections. Zhang, 2012, Nearest neighbor selection for iteratively kNN imputation, J. Syst. Softw., 85, 2541, 10.1016/j.jss.2012.05.073