Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model

Desta Abayechaw

doi:doi:10.11648/j.ijiis.20211006.13

| Peer-Reviewed

Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model

Desta Abayechaw

Published in International Journal of Intelligent Information Systems (Volume 10, Issue 6)

Received: 21 October 2021 Accepted: 17 November 2021 Published: 24 November 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. This package incorporates models for over 42 crops (since Version 4.7.5) as well as tools to facilitate effective use of the models. Tools include database management programs for soil, weather, crop management, and experimental data, utilities, and implementation programs. Crop simulation models simulate growth, development, and yield in accordance with soil-plant-atmosphere dynamics. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to the fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation, and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. In this review paper, I described the approaches used to model the primary scientific components (soil, crop, weather, and management). Besides, the review paper describes the limitations, the future of the DSSAT model, and its importance. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities for its development and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems in the field, farm, and higher levels.

Published in	International Journal of Intelligent Information Systems (Volume 10, Issue 6)
DOI	10.11648/j.ijiis.20211006.13
Page(s)	117-124
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Agronomy, DSSAT Model, Software Program

References

[1]	Alexandrov, V. A., and Hoogenboom, G., (2001). The impact of climate variability and change on crop yield in Bulgaria. Agric. Forest Meteorol. 104, 315–327.
[2]	Baselala, E. (2012). "Agrotech". Fiji Times. Archived from the original on February 1, 2014. Retrieved January 23, 2014.
[3]	Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W.,... & Smith, B. (2007). Modeling the role of agriculture for the 20th-century global terrestrial carbon balance. Global Change Biology, 13 (3), 679-706.
[4]	Boote, K. J., Jones, J. W., Hoogenboom, G., & Pickering, N. B. (1998). The CROPGRO model for grain legumes. In Understanding options for agricultural production: pp. 99-128).
[5]	Boote, K. J., Jones, J. W., Mishoe, J. W., and Wilkerson, G. G., (1986). Modeling growth and yield of groundnut. Agrometeorology of Groundnut: Proceedings of an International Symposium, ICRISAT Sahelian Center, Niamey, Niger. 21/26 Aug 1985, ICRISAT, Patancheru, A. P. 502 324, India, pp. 243/ 254.
[6]	Decision Support System for Agrotechnology Transfer (DSSAT) developed under the International Consortium for Agricultural Systems Applications (ICASA)". Compendium on methods and tools to evaluate impacts of, and vulnerability and adaptation to, climate change. UNFCCC Nairobi Work Programme on impacts, vulnerability, and adaptation to climate change. Retrieved January 23, 2014.
[7]	Fetcher, J., Allison, B. E., Sivakumar, M. V. K., van der Ploeg, R. R., and Bley, J., (1991). An evaluation of the SWATRER and CERES-Millet models for southwest Niger. In: Sivakumar, M. V. K., Wallace, J. S., Renard, C., Giroux, C. (Eds.), Soil Water Balance in the Sudano-Sahhellian Zone. International Association of Hydrological Sciences, Wallingford, UK, pp. 505–513.
[8]	Gijsman, A. J; Jagtap, S. S; Jones, J. W. (2002). "Wading through a swamp of complete confusion: How to choose a method for estimating soil water retention parameters for crop models". European Journal of Agronomy. 18 (1–2): 77–106. doi: 10.1016/S1161-0301(02)00098-9.
[9]	He, J., Jones, J. W., Graham, W. D., and Dukes, M. D. (2010). Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agricultural Systems 103 (5), 256–64. doi: 10.1016/j.agsy.2010.01.006.
[10]	Hoogenboom, G. and White, J. W. (2003). Improving physiological assumptions of simulation models by using gene-based approaches. Agronomy Journal 95 (1), 82–9. doi: 10.2134/agronj2003.0082.
[11]	Hoogenboom, G., C. H. Porter, V. Shelia, K. J. Boote, U. Singh, J. W. White, L. A. Hunt, R. Ogoshi, J. I. Lizaso, J. Koo, S. Asseng, A. Singels, L. P. Moreno, and J. W. Jones. (2019). Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7.5 (https://DSSAT.net). DSSAT Foundation, Gainesville, Florida, USA.
[12]	Hoogenboom, G., Wilkens, P. W., & Tsuji, G. Y. (1999). DSSAT v3, vol. 4. University of Hawaii, Honolulu, HI, 234-235.
[13]	International Benchmark Sites Network for Agrotechnology Transfer. (1993). The IBSNAT Decade. Department of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii, Honoluly, Hawaii.
[14]	International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT). 1989. The Decision Support System for Agrotechnology Transfer Version 2.1 (DSSAT v2.1) User’s Guide. Department of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii, Honolulu, HI.
[15]	Jones, C. A., Kiniry, J. R., & Dyke, P. T. (1986). CERES-Maize: A simulation model of maize growth and development. Texas A& M University Press.
[16]	Jones, G. Hoogenboom, C. H. Porter, K. J. Boote, W. D. Batchelor, L. A. Hunt, P. W. Wilkens, U. Singh, A. J. and, Gijsman, J. T. Ritchie. (2003). The DSSAT cropping system model. Europ. J. Agronomy 18 (2003) 235/265.
[17]	Jones, g. y. Tsuji, g. Hoogenboom, l. a. Hunt, p. k. Thorntow, p. w. Wilkens, d. t. Imamura, w. t. Bowew, and u. singh. (1998). Decision support system for agrotechnology transfer: DSSAT v3.
[18]	Kenneth Boote. (2020). Advances in crop modeling for sustainable agriculture, Burleigh Dodds Science Publishing, Cambridge, UK, 2019, (ISBN: 978 1 78676 240 5; www.bdspublishing.com
[19]	Kihara, J., Fatondji, D., Jones, J. W., Hoogenboom, G., Tabo, R. and Bationo, A. (Eds). (2012). Improving Soil Fertility Recommendations in Africa Using the Decision Support for Agrotechnology Transfer (DSSAT). Springer, Dordrecht, the Netherlands, 195 pp. ISBN: 978-94-007-2959-9.
[20]	Kim, K. S., Yoo, B. H., Shelia, V., Porter, C. H., and Hoogenboom, G. (2018). START: a data preparation tool for crop simulation models using web-based soil.
[21]	Lopez, J. R., Winter, J. M., Elliott, J., Ruane, A. C., Porter, C. H. and Hoogenboom, G. (2017). Integrating growth stage deficit irrigation into a process-based crop model. Agricultural and Forest Meteorology 243 (1), 84–92. doi: 10.1016/j. agrformet.2017.05.001.
[22]	Meng, Lei; Quiring, Steven M. (2008). "A Comparison of Soil Moisture Models Using Soil Climate Analysis Network Observations". Journal of Hydrometeorology. 9 (4): 641. doi: 10.1175/2008JHM916.1.
[23]	Messina, C. D., Jones, J. W., Boote, K. J. and Vallejos, C. E. (2006). A gene-based model to simulate soybean development and yield responses to the environment. Crop Science 46 (1), 456–66. doi: 10.2135/cropsci2005.04-0372.
[24]	Paz, J. O., Batchelor, W. D., Jones, J. W., (2003). Estimating potential economic return for variable rate soybean variety management. Trans. ASAE 46 (4), 1225–1234.
[25]	Paz, J. O., Batchelor, W. D., Tylka, G. L., (2001). Estimating potential economic return for variable rate management in soybeans. Trans. ASAE 44 (5), 1335–1341.
[26]	Porter, Cheryl H.; Jones, J. W.; Adiku, S.; Gijsman, A. J.; Gargiulo, O.; Naab, J. B. (2009). "Modeling organic carbon and carbon-mediated soil processes in DSSAT v4.5". Operational Research. 10 (3): 247. doi: 10.1007/s12351-009-0059-1.
[27]	Quemada, M., Cabrera, M. L., (1995). CERES-N model predictions of nitrogen mineralized from cover crop residues. Soil Sci. Soc. Am. J. 59, 1059–1065.
[28]	Resenes, J., Pavan, W., Holbig, C., Fernandes, J. M. and Hoogenboom, G. (2019). jDSSAT: a JavaScript module for DSSAT-CSM integration. SoftwareX 10. doi: 10.1016/j. softx.2019.100271.
[29]	Ritchie, J. T., Godwin, D. C., & Otter-Nacke, S. (1988). CERES-Wheat. A simulation model of wheat growth and development. Univ. of Tex. Press, Austin.
[30]	Ritchie, J. T.; U. Singh; D. C. Godwin; and W. T. Bowen. (1998). Cereal growth, development, and yield. In Understanding options of agricultural production, ed. G. Y. Tsuji, G. Hoogenboom, P. K. Thornton. Dordrecht, The Netherlands: Kluwer Academic Publishers and International Consortium for Agricultural Systems Applications. 79-98.
[31]	Rosenzweig, C., Allen, L. H., Jr., Jones, J. W., Tsuji, G. Y., Hildebrand, P. (Eds.), Climate Change and Agriculture: Analysis of Potential International impacts (ASA Special Publication No. 59). Amer. Soc. Agron, Madison, WI 1995, p. 382.
[32]	Singh, U., Ritchie, J. T., & Godwin, D. C. (1993). A User's Guide to CERES Rice, V2. 10. University of Ghana http://ugspace.ug.edu.gh 102 Muscle Shoals, AL, USA: International Fertilizer Development Center.
[33]	Hoogenboom, G., White, J. W., Jones, J. W. and Boote, K. J. (1990). BEANGRO V1.00. Dry Bean Crop Growth Simulation Model. User’s Guide. Florida Agricultural Experiment Station Journal No. N-00379. University of Florida, Gainesville, FL, 120 pp. Hoogenboom, G., Jones, J. W. and Boote, K. J. 1991. Predicting Growth and Development of Grain Legumes with a Generic Model. ASAE Hoogenboom, G., Wilkens, P. W. and Tsuji, G. Y. (Eds), DSSAT Version 3 (vol. 4). University of Hawaii, Honolulu, HI, pp. 1–36. Hoogenboom, G., White, J. W. and Messina, C. D. 2004. From genome to crop: integration through simulation modeling. Field Crops Research 90 (1), 145–63. doi: 10.1016/j.fcr.2004.07.014.
[34]	Thorp, K. R.; Bronson, K. F. (2013). "A model-independent open-source geospatial tool for managing point-based environmental model simulations at multiple spatial locations". Environmental Modelling & Software. 50: 25–36. doi: 10.1016/j.envsoft.2013.09.002.
[35]	Thorp, Kelly R.; Dejonge, Kendall C.; Kaleita, Amy L.; Batchelor, William D.; Paz, Joel O. (2008). "Methodology for the use of DSSAT models for precision agriculture decision support". Computers and Electronics in Agriculture. 64 (2): 276. doi: 10.1016/j.compag.2008.05.022.
[36]	Tsuji, G. Y., Uehara, G. and Balas S. (eds.). (1984). DSSAT: A decision support system for agrotechnology transfer, version 3. Vols. 1, 2, and 3. University of Hawaii, Honolulu, HI.
[37]	Tsuji, G. Y., (1998). Network management and information dissemination for agrotechnology transfer. In: Tsuji, G. Y., Hoogenboom, G., Thornton, P. K. (Eds.), Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 367/381.
[38]	Uehara, G., & Tsuji, G. Y. (1991). Progress in crop modeling in the IBSNAT Project. Climatic Risk in Crop Production: Models and Management in the Semi-Arid Tropics and Subtropics. CAB International, Wallingford, pp. 143Á/156.
[39]	Uehara, G., 1998. Synthesis. In: Tsuji, G. Y., Hoogenboom, G., Thornton, P. K. (Eds.), Understanding Options for Agricultural Production. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 389/392.
[40]	Wallach, D., Hwang, C., Correll, M. J., Jones, J. W., Boote, K. J., Hoogenboom, G., Gezan, S., Bhaktae, M. and Vallejos, C. E. (2018). A dynamic model with QTL covariables for predicting the flowering time of common bean (Phaseolus vulgaris) genotypes. European Journal of Agronomy 101 (1), 200–9. doi: 10.1016/j.eja.2018.10.003.
[41]	Welch, S. M., Jones, J. W., Brennan, M. W., Reeder, G., Jacobson, B. M., (2002). PCYield: Model-Based Decision Support for PRIVATE Soybean Production. Agricultural Systems 74 (1), 79/98.
[42]	White, J. W.; Hunt, L. A.; Boote, K. J.; Jones, J. W.; Koo, J.; Kim, S.; Porter, C. H.; Wilkens, P. W.; Hoogenboom, G. (2013). "Integrated description of agricultural field experiments and production: The ICASA Version 2.0 data standards". Computers and Electronics in Agriculture. 96: 1–12. doi: 10.1016/j.compag.2013.04.003.
[43]	Wu, Chunlei; Anlauf, Ruediger; Ma, Youhua (2013). "Application of the DSSAT Model to Simulate Wheat Growth in Eastern China". Journal of Agricultural Science. 5 (5). doi: 10.5539/jas. v5n5p198.
[44]	Yang, J. Y., Drury, C. F., Yang, J. M., Li, Z. T. and Hoogenboom, G. (2014). EasyGrapher: software for data visualization and statistical evaluation of DSSAT cropping system model and the CANB model. International Journal of Computer Theory and Engineering 6 (3), 210–4. doi: 10.7763/ IJCTE. 2014. V6.864.

Cite This Article

Plain Text BibTeX RIS

APA Style

Desta Abayechaw. (2021). Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model. International Journal of Intelligent Information Systems, 10(6), 117-124. https://doi.org/10.11648/j.ijiis.20211006.13

Copy | Download

ACS Style

Desta Abayechaw. Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model. Int. J. Intell. Inf. Syst. 2021, 10(6), 117-124. doi: 10.11648/j.ijiis.20211006.13

Copy | Download

AMA Style

Desta Abayechaw. Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model. Int J Intell Inf Syst. 2021;10(6):117-124. doi: 10.11648/j.ijiis.20211006.13

Copy | Download

@article{10.11648/j.ijiis.20211006.13,
  author = {Desta Abayechaw},
  title = {Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model},
  journal = {International Journal of Intelligent Information Systems},
  volume = {10},
  number = {6},
  pages = {117-124},
  doi = {10.11648/j.ijiis.20211006.13},
  url = {https://doi.org/10.11648/j.ijiis.20211006.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20211006.13},
  abstract = {Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. This package incorporates models for over 42 crops (since Version 4.7.5) as well as tools to facilitate effective use of the models. Tools include database management programs for soil, weather, crop management, and experimental data, utilities, and implementation programs. Crop simulation models simulate growth, development, and yield in accordance with soil-plant-atmosphere dynamics. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to the fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation, and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. In this review paper, I described the approaches used to model the primary scientific components (soil, crop, weather, and management). Besides, the review paper describes the limitations, the future of the DSSAT model, and its importance. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities for its development and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems in the field, farm, and higher levels.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model
AU  - Desta Abayechaw
Y1  - 2021/11/24
PY  - 2021
N1  - https://doi.org/10.11648/j.ijiis.20211006.13
DO  - 10.11648/j.ijiis.20211006.13
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 117
EP  - 124
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20211006.13
AB  - Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. This package incorporates models for over 42 crops (since Version 4.7.5) as well as tools to facilitate effective use of the models. Tools include database management programs for soil, weather, crop management, and experimental data, utilities, and implementation programs. Crop simulation models simulate growth, development, and yield in accordance with soil-plant-atmosphere dynamics. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to the fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation, and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. In this review paper, I described the approaches used to model the primary scientific components (soil, crop, weather, and management). Besides, the review paper describes the limitations, the future of the DSSAT model, and its importance. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities for its development and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems in the field, farm, and higher levels.
VL  - 10
IS  - 6
ER  -

Copy | Download

Author Information

Desta Abayechaw

Ethiopia Institute of Agricultural Research, Wondo Genet Agricultural Research Center, Hawassa, Ethiopia

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Desta Abayechaw. (2021). Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model. International Journal of Intelligent Information Systems, 10(6), 117-124. https://doi.org/10.11648/j.ijiis.20211006.13

Copy | Download

ACS Style

Desta Abayechaw. Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model. Int. J. Intell. Inf. Syst. 2021, 10(6), 117-124. doi: 10.11648/j.ijiis.20211006.13

Copy | Download

AMA Style

Desta Abayechaw. Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model. Int J Intell Inf Syst. 2021;10(6):117-124. doi: 10.11648/j.ijiis.20211006.13

Copy | Download

@article{10.11648/j.ijiis.20211006.13,
  author = {Desta Abayechaw},
  title = {Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model},
  journal = {International Journal of Intelligent Information Systems},
  volume = {10},
  number = {6},
  pages = {117-124},
  doi = {10.11648/j.ijiis.20211006.13},
  url = {https://doi.org/10.11648/j.ijiis.20211006.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20211006.13},
  abstract = {Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. This package incorporates models for over 42 crops (since Version 4.7.5) as well as tools to facilitate effective use of the models. Tools include database management programs for soil, weather, crop management, and experimental data, utilities, and implementation programs. Crop simulation models simulate growth, development, and yield in accordance with soil-plant-atmosphere dynamics. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to the fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation, and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. In this review paper, I described the approaches used to model the primary scientific components (soil, crop, weather, and management). Besides, the review paper describes the limitations, the future of the DSSAT model, and its importance. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities for its development and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems in the field, farm, and higher levels.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model
AU  - Desta Abayechaw
Y1  - 2021/11/24
PY  - 2021
N1  - https://doi.org/10.11648/j.ijiis.20211006.13
DO  - 10.11648/j.ijiis.20211006.13
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 117
EP  - 124
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20211006.13
AB  - Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. This package incorporates models for over 42 crops (since Version 4.7.5) as well as tools to facilitate effective use of the models. Tools include database management programs for soil, weather, crop management, and experimental data, utilities, and implementation programs. Crop simulation models simulate growth, development, and yield in accordance with soil-plant-atmosphere dynamics. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to the fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation, and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. In this review paper, I described the approaches used to model the primary scientific components (soil, crop, weather, and management). Besides, the review paper describes the limitations, the future of the DSSAT model, and its importance. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities for its development and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems in the field, farm, and higher levels.
VL  - 10
IS  - 6
ER  -

Copy | Download