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Relevant bibliographies by topics / Crop forecasting

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Academic literature on the topic 'Crop forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Crop forecasting"

1

Mariño, Miguel A., John C. Tracy, and S. Alireza Taghavi. "Forecasting of reference crop evapotranspiration." Agricultural Water Management 24, no. 3 (1993): 163–87. http://dx.doi.org/10.1016/0378-3774(93)90022-3.

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Buklagin, D. S. "Agricultural crop yield forecasting methods." Machinery and Equipment for Rural Area, no. 12 (December 20, 2020): 25–28. http://dx.doi.org/10.33267/2072-9642-2020-12-25-28.

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The main areas of the development and use of digital technologies and systems for forecasting the yield of agricultural crops based on satellite data are described. Proposals are given for the development of research in the field of the use of space technologies and their widespread use in agriculture.

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Veenadhari, Dr S. "Crop Advisor: A Software Tool for Forecasting Paddy Yield." Bonfring International Journal of Data Mining 6, no. 3 (2016): 34–38. http://dx.doi.org/10.9756/bijdm.10461.

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Gotsch, N., and P. Rieder. "Forecasting future developments in crop protection." Crop Protection 9, no. 2 (1990): 83–89. http://dx.doi.org/10.1016/0261-2194(90)90083-j.

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MOHAN, S., and N. ARUMUGAM. "Forecasting weekly reference crop evapotranspiration series." Hydrological Sciences Journal 40, no. 6 (1995): 689–702. http://dx.doi.org/10.1080/02626669509491459.

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Stone, Roger C., and Holger Meinke. "Operational seasonal forecasting of crop performance." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (2005): 2109–24. http://dx.doi.org/10.1098/rstb.2005.1753.

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Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. C

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Ben Dhiab, Ali, Mehdi Ben Mimoun, Jose Oteros, et al. "Modeling olive-crop forecasting in Tunisia." Theoretical and Applied Climatology 128, no. 3-4 (2016): 541–49. http://dx.doi.org/10.1007/s00704-015-1726-1.

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Jain, R. C., and Ranjana Agrawal. "Probability Model for Crop Yield Forecasting." Biometrical Journal 34, no. 4 (1992): 501–11. http://dx.doi.org/10.1002/bimj.4710340410.

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Biibosunov, Bolotbek, Baratbek Sabitov, Saltanat Biibosunova, Zhamin Sheishenov, Sharshenbek Zhusupkeldiev, and Zhyldyz Mamadalieva. "Machine learning for crop yield forecasting." Cybernetics and Physics, Volume 12, 2023, Number 3 (November 30, 2023): 174–81. http://dx.doi.org/10.35470/2226-4116-2023-12-3-174-181.

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Amid the persistent rise in global population, there has been a heightened focus on food security by academia, governmental initiatives, and international endeavors. Food security serves as a critical pillar in the national security framework, contributing to a nation’s sovereignty and self-sufficiency in food supply. To fulfill global requirements for essential food items, there is an imperative need to enhance agricultural efficiency across countries. Concurrently, agricultural practices must align with contemporary quality standards and meet consumer needs, drawing upon an integrated approa

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Bhatakar, Ajinkya, Lalit Tayde, Sandesh Raut, Ankit Pakhare, Tejas Bavaskar, and Shivaji Chavhan. "AGRICULTURE CROP PRICE PREDICTION USING MACHINE LEARNING." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–9. https://doi.org/10.55041/isjem02521.

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The accurate forecast of crop prices is of considerable importance to farmers, policymakers, and stakeholders to enable informed decisions and ensure economic stability in the agricultural sector. The traditional forecasting methods are largely ineffective when it comes to accurate forecasting, due to the complex and dynamic nature of the agricultural market. This study proposes a machine learning based solution to effectively forecast crop prices. Various machine learning models such as regression, decision trees, and neural networks were used to analyze and forecast crop prices using histori

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Dissertations / Theses on the topic "Crop forecasting"

1

McVean, Ross Iolo Kester. "Forecasting pea aphid outbreaks." Thesis, University of East Anglia, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389386.

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Stephens, David J. "Crop yield forecasting over large areas in Australia." Thesis, Stephens, David J (1995) Crop yield forecasting over large areas in Australia. PhD thesis, Murdoch University, 1995. https://researchrepository.murdoch.edu.au/id/eprint/51647/.

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Inter-annual variations in crop yield are intricately linked to fluctuations in the weather. Accurate yield forecasts prior to harvest are possible if crop-weather relationships are integrated into models that are responsive to the major yield determining factors. A network of meteorological stations was selected across the Australian wheat belt and monthly rainfall regressed with wheat yields from the surrounding shires. Autumn rains that permit an early sowing and finishing rains after July are important for higher yields. As the rainfall distribution becomes more winter dominant in natu

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Kantanantha, Nantachai. "Crop decision planning under yield and price uncertainties." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/24676.

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Thesis (Ph.D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007.<br>Committee Co-Chair: Griffin, Paul; Committee Co-Chair: Serban, Nicoleta; Committee Member: Liang, Steven; Committee Member: Sharp, Gunter; Committee Member: Tsui, Kwok-Leung

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Orlowski, Jan Alexander Kazimierz. "The ENSO Cycle and Predictability of US Crop Yields." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17166.

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While the impacts of the El Nino Southern Oscillation (ENSO) are well documented on topics ranging from agricultural production to socio-economic factors, a closer consideration of key interaction terms in this complex relationship is pivotal for better understanding of future production impacts and as well as relevant policy implications. In this thesis, the ENSO link to staple crop production in the US is derived through an econometric approach, in particular taking advantage of recent advances in the nonlinear parameterization of climate variables such as temperature. Via the comparison of

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Eggerman, Christopher Ryan. "Projecting net incomes for Texas crop producers: an application of probabilistic forecasting." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4134.

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Agricultural policy changes directly affect the economic viability of Texas crop producers because government payments make up a significant portion of their net farm income (NFI). NFI projections benefit producers, agribusinesses and policy makers, but an economic model making these projections for Texas did not previously exist. The objective of this study was to develop a model to project annual NFI for producers of major crops in Texas. The Texas crop model was developed to achieve this objective, estimating state prices, yields and production costs as a function of their national counterp

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Teo, Chee-Kiat. "Application of satellite-based rainfall estimates to crop yield forecasting in Africa." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434333.

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Osman, E. M. H. "Crop yield forecasting at national and regional levels using remote sensing techniques." Thesis, Cranfield University, 2003. http://dspace.lib.cranfield.ac.uk/handle/1826/11058.

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Crop yield forecasting models are needed to help farmers and decision makers cheaply detect crop condition early enough to assess and mitigate its impacts on grain production. A precise estimate of crop production requires an accurate measure of the total cultivated area and well-established knowledge of crop yield. The first requirement is no longer a problem as is technically solved through various techniques such as area frame sampling. With respect to the second, great efforts have been made to find an accurate definition of the crop yield with respect to the actual factors that shape its

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Osman, El Mamoun H. "Crop yield forecasting at national and regional levels using remote sensing techniques." Thesis, Cranfield University, 2003. http://dspace.lib.cranfield.ac.uk/handle/1826/11058.

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Crop yield forecasting models are needed to help farmers and decision makers cheaply detect crop condition early enough to assess and mitigate its impacts on grain production. A precise estimate of crop production requires an accurate measure of the total cultivated area and well-established knowledge of crop yield. The first requirement is no longer a problem as is technically solved through various techniques such as area frame sampling. With respect to the second, great efforts have been made to find an accurate definition of the crop yield with respect to the actual factors that shape its

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Soares, Abilio Barros. "Crop Price and Land Use Change: Forecasting Response of Major Crops Acreage to Price and Economic Variables in North Dakota." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27685.

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The objective of this study is to examine land use change for cropping systems in North Dakota. Using Seemingly Unrelated Regression with full information maximum likelihood estimation method, acreage forecasting models for barley, corn, oats, soybean, and wheat were developed to examine the extent to which farmers? expectations of prices and costs affect their crop choices. The results of the study show that farmers? decision for acreage allocation is varied across the crops depending on how responsive they are to price, cost and yield of its own and competing crops. Substitutability and comp

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Johnson, Michael David. "Crop yield forecasting on the Canadian Prairies by satellite data and machine learning methods." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45281.

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The production of grain crops plays an important role in the economy of the Canadian Prairies and early reliable crop yield forecasts over large areas would help policy makers and grain marketing agencies in planning for exports. Forecast models developed from satellite data have the potential to provide quantitative and timely information on agricultural crops over large areas. The use of nonlinear modeling techniques from the field of machine learning could improve crop forecasting from the linear models most commonly used today. The Canadian Prairies consist of the provinces of Alberta, Sas

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Books on the topic "Crop forecasting"

1

Vogel, Fred A. Understanding USDA crop forescasts. U.S. Dept. Agriculture, National Agricultural Statistics Service and Office of the Chief Economist World Agricultural Outlook Board, 1999.

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Ontario. Ministry of Natural Resources. and Ontario Forest Resources Group, eds. Guidelines for tree seed crop forecasting and collecting. The Ministry, 1986.

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R, Creasey K., and Ontario. Ministry of Natural Resources., eds. Guidelines for tree seed crop forecasting and collecting. The Ministry, 1996.

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Vogel, Fred A. Review of Chinese crop production forecasting and estimation methology. U.S. Dept. of Agriculture, National Agricultural Statistics Service, 1999.

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Biot, Yvan. Crop production forecasting based on long term climate predictions. School of Development Studies, University of East Anglia, 1991.

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N, Podoĭnit͡s︡yna Z., Khudi͡a︡kova A. I, and Gosudarstvennyĭ komitet SSSR po gidrometeorologii i kontroli͡u︡ prirodnoĭ sredy., eds. Agrometeorologii͡a︡. Gidrometeoizdat, 1985.

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Ethiopia. Early Warning and Planning Services., ed. Food supply of the crop dependent population in 1990. Early Warning and Planning Services, Relief and Rehabilitation Commission, 1990.

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Ethiopia. Early Warning and Planning Services., ed. Food supply prospect of the crop and livestock dependent population in 1992. Early Warning and Planning Services, Relief and Rehabilitation Commission, 1992.

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Lee, Juhwan, Steven De Gryze, and Johan Six. Effect of climate change on field crop production in the Central Valley of California: Final paper. California Energy Commission, 2009.

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Ethiopia. Early Warning and Planning Services., ed. 1985 meher (main) crop season synoptic and 1986 food supply prospect final report. Early Warning and Planning Services, Relief and Rehabilitation Commission, 1986.

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Book chapters on the topic "Crop forecasting"

1

Singh, D., and M. P. Jha. "Statistical Problems in Crop Forecasting." In A Celebration of Statistics. Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4613-8560-8_25.

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Makarovskikh, Tatiana, Anatoly Panyukov, and Mostafa Abotaleb. "Monitoring and Forecasting Crop Yields." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38864-4_6.

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Meena, Mukesh, and Pramod Kumar Singh. "Crop Yield Forecasting Using Neural Networks." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03756-1_29.

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Pan, Haizhu, and Zhongxin Chen. "Crop Growth Modeling and Yield Forecasting." In Springer Remote Sensing/Photogrammetry. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66387-2_11.

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Verma, Swayam, Shashwat Sinha, Pratima Chaudhury, Sushruta Mishra, and Ahmed Alkhayyat. "Crop Yield Forecasting with Precise Machine Learning." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3010-4_38.

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Bhilare, Amol, Debabrata Swain, and Manish Kumar. "AI-Powered Crop Yield Forecasting: A Metaphoric Exploration." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4883-2_27.

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Anwar, Kiran, Lotfi Mustapha Kazi-Tani, and Metin Türkay. "Advanced Data Analytical Methods for Citrus Crop Yield Forecasting." In Greening of Industry Networks Studies. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-63793-3_9.

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Bouman, B. A. M., C. A. van Diepen, P. Vossen, and T. van der Wal. "Simulation and systems analysis tools for crop yield forecasting." In Applications of Systems Approaches at the Farm and Regional Levels Volume 1. Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5416-1_24.

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Mota, Fernando Silveira da, Marisa Oliveira de Oliveira Agendes, José Luiz da Costa Rosskoff, and João Baptista da Silva. "Modeling and Forecasting Brazilian Crop Yields Using Meteorological Data." In Agroclimate Information for Development. Routledge, 2022. http://dx.doi.org/10.4324/9780429049323-27.

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Senthamarai Kannan, K., K. M. Karuppasamy, and R. Balasubramaniam. "Fuzzy Time Series Model for Forecasting Agricultural Crop Production." In Advances in Data Science and Management. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5685-9_37.

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Conference papers on the topic "Crop forecasting"

1

Shanmugasundaram, C., C. Umamaheswari, A. Vijayalakshmi, and Prabha Elizabeth Varghese. "Crop for Est - Crop Forecasting and Estimation. Crop Yield Estimation and Profitability Analysis for Precision Agriculture." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10893947.

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Vaishnavi, D., R. Bavithra, M. Rufina Marssha, and S. Sowmiya. "Agriculture Crop Yield Forecasting using Deep Learning Techniques." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00093.

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Gupta, Raji, Shiv Prakash Singh, Rishabh Gupta, Saurabh, and Shikha Agarwal. "Machine Learning-based Soil Analysis for Crop Forecasting." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10941503.

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Jadhav, Balasaheb, Vaidehi Ligde, Rushikesh Malpani, Rushikesh Lakhotiya, and Phalguni Savale. "CropOptimizer: Intelligent Crop Forecasting and Decision Support System." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862107.

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D, Chitra Devi, Mahalakshmi G, Priyadharshini M, and Reshena R. "Forecasting Crop Yields Using Machine Learning Techniques For Sustainable Farming." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026365.

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Chanti, Suragali, Kasichainula Vydehi, Hima Keerthi Penumatsa, K. Narayana Rao, Nynalasetti Kondala Kameswara Rao, and A. Lakshmanarao. "Crop yield forecasting through novel machine learning dynamic ensemble selection techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725479.

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Kruthika, P., K. Srinidhi, M. Sowmithra, S. Velan Karthick, and A. Siva Prasad. "Unified Forecasting Models: Crop Recommendation using Predicted Rainfall with Machine Learning." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866035.

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Kollu, Praveen Kumar, Niranjani Raavi, and Dharani Duggineni. "Agro-smart Crop Recommendation and Yield Forecasting Employed through Machine Learning." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10941104.

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Srivallidevi, V., and Veera V. Rama Rao M. "A Robust Crop Yield Forecasting Model Through Machine Learning Ensemble Techniques." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910450.

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Goel, Paurav, Bhumika Tiwari, Banisha Sharma, and Priyanka Chopra. "Enhancing Crop Yield Prediction Through Time Series Forecasting with XGBOOST Algorithm." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011405.

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Reports on the topic "Crop forecasting"

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.06. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.06.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecasts by AKADEMIYA2063 aim to provide more accurate and timely statistics about harvest and yield levels for nine crops across 47 African countries. Developed at AKADEMIYA2063, the Africa Crop Production (AfCP) model is an artificial intelligence (AI) based forecasting model applied to remotely sensed bio-geophysical data to produce estimates of expected crop yields and harvests at the beginning of every growing season.

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.08. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.08.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 8 by AKADEMIYA2063 provides accurate and timely millet production statistics for Guinea using the Africa Crop Production (AfCP) model. The AfCP developed at AKADEMIYA2063 is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to produce estimates of crop yields and harvests at the beginning of every growing season for nine crops in 47 African countries.

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Ndoye, Aïssatou, Khadim Dia, and Racline Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.05. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.05.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 5 by AKADEMIYA2063 provides timely and accurate statistics on millet production in Sierra Leone using the Africa Crop Production (AfCP) model. Developed at AKADEMIYA2063, the AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed bio-geophysical data to produce estimates of expected crop yields and harvests at the beginning of every growing season for nine crops across 47 African countries.

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.09. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.09.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 9 by AKADEMIYA2063 aims to provide timely and accurate statistics of millet production in Guinea-Bissau using the Africa Crop Production (AfCP) model developed at AKADEMIYA2063. The AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to estimate crop yields and harvests at the beginning of every growing season for nine crops across 47 African countries.

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.01. AKADEMIYA2063, 2022. http://dx.doi.org/10.54067/acpf.01.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecasts by AKADEMIYA2063 aim to provide more accurate and timely statistics about harvest and yield levels for nine key crops across nearly 50 African countries. Developed at AKADEMIYA2063, the Africa Crop Production (AfCP) model is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to produce estimates of expected crop yields and harvests at the beginning of every growing season.

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.03. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.03.

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Brief 3 presents forecasts on millet production levels in Côte d’Ivoire based on AKADEMIYA2063’s Africa Crop Production (AfCP) model. The AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed bio-geophysical data to produce estimates of crop production for nine crops in 47 African countries before the harvesting period. Thus, millet production statistics in Côte d’Ivoire are presented in this brief.

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.07. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.07.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 7 by AKADEMIYA2063 provides accurate and timely statistics about millet production in Mali. The Africa Crop Production (AfCP) model developed at AKADEMIYA2063 is used to forecast millet production. The AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed bio-geophysical data to estimate expected crop yields and harvests at the beginning of every growing season for nine crops across nearly 47 African countries.

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Ndoye, Aissatou, Khadim Dia, and Racine Ly. The AAgWa Crop Production Forecasts Brief Series - Issue N.02. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.02.

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The Africa Agriculture Watch (AAgWa) Crop Production Brief 2, produced by AKADEMIYA2063, aims to provide more accurate and timely statistics on millet production in Gambia using the Africa Food Crop Production (AfCP) model. The AfCP developed at AKADEMIYA2063 is an artificial intelligence (AI) based forecasting model used to produce yield and harvest forecasts at the beginning of each growing season for nine crops in 47 African countries.

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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.10. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.10.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 10 aims to provide more accurate and timely statistics about millet production in Ghana using the Africa Crop Production (AfCP) model. Developed at AKADEMIYA2063, the AfCP is an artificial intelligence (AI) based forecasting model applied to remotely sensed geo-biophysical data to produce estimates of expected crop yields and harvests at the beginning of every growing season.

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Thomas, Samuel, Elaine Baker, Kamal Aryal, et al. Towards Climate Resilient Agriculture in Nepal: Solutions for smallholder farmers. International Centre for Integrated Mountain Development (ICIMOD), 2024. https://doi.org/10.53055/icimod.1077.

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Nepal, one of the world’s most climate-vulnerable countries, is facing severe impacts from climate change, particularly in its agricultural sector, which employs two-thirds of the population and contributes more than a quarter of the nation’s GDP. Smallholder farmers, the backbone of this sector, are grappling with rising temperatures, erratic monsoon patterns, droughts, and increasingly frequent extreme weather events. Adapting to these challenges through Climate-Resilient Agriculture (CRA) is essential to ensuring food security and safeguarding the livelihoods of millions. CRA incorporates n

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