Hostname: page-component-76fb5796d-r6qrq Total loading time: 0 Render date: 2024-04-25T10:04:42.619Z Has data issue: false hasContentIssue false
  • English
  • Français

UNRAVELLING CROP WATER PRODUCTIVITY OF TEF (ERAGROSTIS TEF (ZUCC.) TROTTER) THROUGH AQUACROP IN NORTHERN ETHIOPIA

Published online by Cambridge University Press:  25 November 2011

ALEMTSEHAY TSEGAY ,
DIRK RAES ,
SAM GEERTS ,
ELINE VANUYTRECHT ,
BERHANU ABRAHA ,
JOZEF DECKERS ,
HANS BAUER  and
KINDEYA GEBREHIWOT
ALEMTSEHAY TSEGAY*
Affiliation:
Department of Dryland Crop and Horticultural Sciences, Mekelle University, P.O. Box 231, Mekelle, Ethiopia Division of Soil and Water Management, K.U. Leuven University, Celestijnenlaan 200E-2411, B-3001 Leuven, Belgium
DIRK RAES
Affiliation:
Division of Soil and Water Management, K.U. Leuven University, Celestijnenlaan 200E-2411, B-3001 Leuven, Belgium
SAM GEERTS
Affiliation:
Division of Soil and Water Management, K.U. Leuven University, Celestijnenlaan 200E-2411, B-3001 Leuven, Belgium
ELINE VANUYTRECHT
Affiliation:
Division of Soil and Water Management, K.U. Leuven University, Celestijnenlaan 200E-2411, B-3001 Leuven, Belgium
BERHANU ABRAHA
Affiliation:
Department of Dryland Crop and Horticultural Sciences, Mekelle University, P.O. Box 231, Mekelle, Ethiopia
JOZEF DECKERS
Affiliation:
Division of Soil and Water Management, K.U. Leuven University, Celestijnenlaan 200E-2411, B-3001 Leuven, Belgium
HANS BAUER
Affiliation:
VLIR-UOS Ethiopia, P.O. Box 80522, Addis Ababa, Ethiopia/P.O. Box 231, Mekelle, Ethiopia
KINDEYA GEBREHIWOT
Affiliation:
Department of Land Resource Management and Environmental Protection, Mekelle University, P.O. Box 231, Mekelle, Ethiopia
*
§Corresponding author. Email: alemtsehay.tsegay@gmail.com; alemtsehaytsegay@yahoo.com
Get access Rights & Permissions [Opens in a new window]

Summary

At various locations in North Ethiopia (Tigray), field experiments were conducted from 2006 to 2009 to assess the crop response to water stress of tef (Eragrostis tef (Zucc.) Trotter) under rainfed, fully irrigated and deficit irrigation conditions. Observed soil water content (SWC), canopy cover (CC), biomass production (B) and final grain yield (Y) were used to calibrate and validate AquaCrop for tef. Data from an experiment in a controlled environment in 2008 were also considered in the calibration process. Simulations of SWC, CC, B and Y were evaluated by determining the index of agreement, the root mean square error, the coefficient of determination and the Nash–Sutcliffe efficiency. The statistical parameters showed an adequate fit between observations and simulations. The model was able to simulate for tef growing under rainfed condition the observed fast drop in SWC and CC when the rains ceased. The overall goodness of fit between the observed and simulated CC and SWC indicated that the thresholds for root zone depletion at which water stress (i) affects canopy development, (ii) induces stomata closure and (iii) triggers early canopy senescence were well selected. The normalised biomass water productivity (WP*) for tef was 14 g m−2 for the local variety and 21 g m−2 for the improved variety, which is a lot smaller than the WP* expected for C4 plants (30–35 g m−2). The results revealed an increase of 27% in reference harvest index (HIo) of tef in response to mild water stress during the yield formation of up to 33%. However, severe water stress causing stomata closure had a negative effect on HIo. Once it is properly calibrated, AquaCrop can provide room to improve the water productivity of tef by developing guidelines for good agricultural management strategies.

Type
Research Article
Information
Experimental Agriculture , Volume 48 , Issue 2 , April 2012 , pp. 222 - 237
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration (guidelines for computing crop water requirements). FAO Irrigation and Drainage Paper 56. Rome, Italy: FAO.Google Scholar
Araya, A., Keesstra, S. D. and Stroosnijder, L. (2010). Simulating yield response to water of Teff (Eragrostis tef) with FAO's AquaCrop model. Field Crops Research 116:196–204.CrossRefGoogle Scholar
Azam-Ali, S. N. and Squire, G. R. (2002). Principles of Tropical Agronomy. Wallingford, UK: CABI International.Google Scholar
Central Statistical Agency of Ethiopia (CSA) (2000–2010). Agricultural Sample Survey 2000–2010. Report on area and production of major crops (Meher season), Statistical Bulletin 227, 245, 302, 331, 361, 388, 417 and 446. Addis Ababa, Ethiopia: CSA.Google Scholar
Central Statistical Agency of Ethiopia (CSA) (2010). Agricultural Sample Survey 2009/2010. Report on area and production of major crops (Meher season), Statistical Bulletin 446. Addis Ababa, Ethiopia: CSA.Google Scholar
Dejene, K. Mengistu (2009). The influence of soil water deficit imposed during various developmental phases on physiological processes of tef (Eragrostis tef). Agriculture, Ecosystems and Environment 132:283–289.Google Scholar
Ethiopian Agricultural Research Organization (EARO) (2002). Crop Research Directorate: Research Recommendations for Improved Crop Production. EARO: Addis Ababa, Ethiopia.Google Scholar
Farré, I. and Faci, J. M. (2006). Comparative response of maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) to deficit irrigation in a Mediterranean environment. Agricultural Water Management 83:135–143.CrossRefGoogle Scholar
Garcia Vila, M., Fereres, E., Mateos, L., Orgaz, F. and Steduto, P. (2009). Deficit irrigation optimization of cotton with AquaCrop. Agronomy Journal 101:477–487.CrossRefGoogle Scholar
Geerts, S. and Raes, D. (2009). Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agricultural Water Management 96:1275–1284.CrossRefGoogle Scholar
Geerts, S., Raes, D., Garcia, M., Miranda, R., Cusicanqui, J., Taboada, C., Mendoza, J., Huanca, R., Mamani, A., Octavio, C., Mamani, J., Morales, B., Osco, V. and Steduto, P. (2009). Simulating yield response of quinoa (Chenopodium quinoa Willd.) to water availability with AquaCrop. Agronomy Journal 101:499–508.CrossRefGoogle Scholar
Heng, L. K., Hsiao, T., Evett, S., Howell, T. and Steduto, P. (2009). Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agronomy Journal 101:488–498.CrossRefGoogle Scholar
Hsiao, T. C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D. and Fereres, E. (2009). AquaCrop – The fAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agronomy Journal 101:448–459.CrossRefGoogle Scholar
Karcher, D. E. and Richardson, M. D. (2005). Batch analysis of digital images to evaluate turfgrass characteristics. Crop Science 45:1536–1539.CrossRefGoogle Scholar
Karunaratne, A. S., Azam-Ali, S. N., Al-Shareef, I., Sesay, A., Jørgensen, S. T. and Crout, N. M. J. (2010). Modeling the canopy development of bambara groundnut. Agricultural and Forest Meteorology 150 (7–8):1007–1015.CrossRefGoogle Scholar
Loague, K. and Green, R. E. (1991). Statistical and graphical methods for evaluating solute transport models: overview and application. Journal of Contaminant Hydrology 7:51–73.CrossRefGoogle Scholar
Nash, J. E. and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. Part 1 – a discussion of principles. Journal of Hydrology 10 (3):282–290.CrossRefGoogle Scholar
National Research Council (NRC), Board on Science and Technology for International Development (1996). Lost Crops of Africa. Vol. I: Grains. Washington, DC: National Academy Press.Google Scholar
Nyssen, J., Vandenreyken, H., Poesen, J., Moeyersons, J., Deckers, J., Haile, M., Salles, C. and Govers, G. (2005). Rainfall erosivity and variability in the Northern Ethiopian highlands. Journal of Hydrology 311:172–187.CrossRefGoogle Scholar
Raes, D., Steduto, P., Hsiao, T. C. and Fereres, E. (2009). AquaCrop – the FAO crop model to predict yield response to water: II. Main algorithms and software description. Agronomy Journal 101:438–447.CrossRefGoogle Scholar
Raes, D., Steduto, P., Hsiao, T. C. and Fereres, E. (2010). Reference Manual AquaCrop Version 3.1. Available from: http://www.fao.org/nr/water/aquacrop.html [Accessed 11th January 2011].Google Scholar
Saxton, K. E. and Rawls, W. J. (2006). Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal 70:1569–1578.CrossRefGoogle Scholar
Seyfu, K. (1997). Tef (Eragrostis tef (Zucc.) Trotter). Promoting the Conservation and Use of Underutilized and Neglected Crops. 12. Institute of Plant Genetics and Crop Plant Research, Gatersleben/International Plant Genetic Resources Institute, Rome: Italy.Google Scholar
SPSS Inc. (1990–2000). Sigma Scan Pro 5.0. Chicago, IL: SPSS Science Marketing Department.Google Scholar
Steduto, P. and Albrizio, R. (2005). Resource use efficiency of field-grown sunflower, sorghum, wheat and chickpea II. Water use efficiency and comparison with radiation use efficiency. Agricultural and Forest Meteorology 130:269–281.CrossRefGoogle Scholar
Steduto, P., Hsiao, T. C. and Fereres, E. (2007). On the conservative behavior of biomass water productivity. Irrigation Science 25:189–207.CrossRefGoogle Scholar
Steduto, P., Hsiao, T. C., Raes, D. and Fereres, E. (2009). AquaCrop – the FAO crop model to predict yield response to water: I. Concepts and underlying principles. Agronomy Journal 101:426–437.CrossRefGoogle Scholar
Vavilov, N. I. (1951). The Origin, Variation, Immunity and Breeding of Cultivated Plants. New York: The Cronica Botanica Co., Stechert-Hafner, Inc.CrossRefGoogle Scholar
Willmott, C. J. 1982. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63:1309–1313.2.0.CO;2>CrossRefGoogle Scholar