Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2025-01-05T12:33:11.907Z Has data issue: false hasContentIssue false
  • English
  • Français

SOWING STRATEGIES FOR BARLEY (HORDEUM VULGARE L.) BASED ON MODELLED YIELD RESPONSE TO WATER WITH AQUACROP

Published online by Cambridge University Press:  18 January 2012

ABRHA BERHANU ,
NELE DELBECQUE ,
DIRK RAES ,
ALEMTSEHAY TSEGAY ,
MLADEN TODOROVIC ,
LEE HENG ,
ELINE VANUTRECHT ,
SAM GEERTS ,
MARGA GARCIA-VILA  and
SEPPE DECKERS
ABRHA BERHANU*
Affiliation:
Department of Crop and Horticultural Sciences, Mekelle University, PO Box, 231, Mekelle, Ethiopia Department of Earth and Environmental Sciences, K.U. Leuven University, Celestijnenlaan 200E-pb 2411, 3001 Heverlee, Belgium
NELE DELBECQUE
Affiliation:
Department of Earth and Environmental Sciences, K.U. Leuven University, Celestijnenlaan 200E-pb 2411, 3001 Heverlee, Belgium
DIRK RAES
Affiliation:
Department of Earth and Environmental Sciences, K.U. Leuven University, Celestijnenlaan 200E-pb 2411, 3001 Heverlee, Belgium
ALEMTSEHAY TSEGAY
Affiliation:
Department of Crop and Horticultural Sciences, Mekelle University, PO Box, 231, Mekelle, Ethiopia Department of Earth and Environmental Sciences, K.U. Leuven University, Celestijnenlaan 200E-pb 2411, 3001 Heverlee, Belgium
MLADEN TODOROVIC
Affiliation:
Mediterranean Agronomic Institute of Bari, Via Cegalie 9, 70010, Valenzano (BA), Italy
LEE HENG
Affiliation:
Soil and Water Management and Crop Nutrition Section, Wagrammer Strasse 5, PO Box 100, 1400 Vienna, Austria
ELINE VANUTRECHT
Affiliation:
Department of Earth and Environmental Sciences, K.U. Leuven University, Celestijnenlaan 200E-pb 2411, 3001 Heverlee, Belgium
SAM GEERTS
Affiliation:
VLIR-UOS University Development Cooperation, Bolwerksquare 1a, 1050 Brussels, Belgium
MARGA GARCIA-VILA
Affiliation:
Spanish Council for Scientific Research, Institute for Sustainable Agriculture, 14080-Cordoba, Spain
SEPPE DECKERS
Affiliation:
Department of Earth and Environmental Sciences, K.U. Leuven University, Celestijnenlaan 200E-pb 2411, 3001 Heverlee, Belgium
*
‡Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

AquaCrop, the FAO water productivity model, is used as a tool to predict crop production under water limiting conditions. In the first step AquaCrop was calibrated and validated for barley (Hordeum vulgare L.). Data sets of field experiments at seven different locations in four countries (Ethiopia, Italy, Syria and Montana, USA) with different climates in different years and with five different cultivars were used for model calibration and validation. The goodness-of-fit between observed and simulated soil water content, green canopy cover, biomass and grain yield was assessed by means of the coefficient of determination (R2), the Nash–Sutcliff efficiency (E), the index of agreement (d) and the root mean square error (RMSE). The statistical parameters indicated an adequate accuracy of simulations (validation regression of yield: R2 = 0.95, E = 0.94, d = 0.99, RMSE = 0.34). Subsequently, sowing strategies in the semi-arid environment of northern Ethiopia were evaluated with the validated model. Dry sowing had a probability of 47% germination failure attributable to false start of the rainy season. On the other hand, delay sowing at the start of the rainy season to eliminate germinating weeds should be kept as short as possible because grain yields strongly reduce in the season due to water stress when sowing is delayed on shallow soils. This research demonstrates the ability of AquaCrop to predict accurately crop performance with only a limited set of input variables, and the robustness of the model under various environmental and climatic conditions.

Type
Research Article
Information
Experimental Agriculture , Volume 48 , Issue 2 , April 2012 , pp. 252 - 271
Copyright
Copyright © Cambridge University Press 2012

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

Albrizio, R., Todorovic, M., Matic, T. and Stellacci, A. M. (2010). Comparing the interactive effects of water and nitrogen on durum wheat and barley grown in a Mediterranean environment. Field Crops Research 115:179–190.CrossRefGoogle Scholar
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 No. 56, Rome, Italy, 300 pp.Google Scholar
Araya, A., Habtu, S., Hadgu, K. M., Kebede, A. and Dejene, T. (2010). Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare). Agricultural Water Management 97:1838–1846.CrossRefGoogle Scholar
Araya, A. and Stroosnijder, L. 2010. Assessing drought risk and irrigation need in northern Ethiopia. Agricultural and Forest Meteorology 151:425–436.CrossRefGoogle Scholar
Aspinall, D. 1965. The effects of soil moisture stress on the gowth of barley – II. Grain growth. Australian Journal of Agricultural Research 16:265–275.CrossRefGoogle Scholar
Babadoost, M., Mathre, D. E., Johnston, R. H. and Bonde, M. R. (2004). Survival of teliospores of Tilletia indica in soil. Plant Disease 88:56–62.CrossRefGoogle ScholarPubMed
Belmans, C., Wesseling, J. G. and Feddes, R. A. (1983). Simulation model of the water balance of a cropped soil: SWATRE. Journal of Hydrology 63:271–286.CrossRefGoogle Scholar
Bewket, W. and Conway, D. 2007. A note on the temporal and spatial variability of rainfall in the drought-prone Amhara region of Ethiopia. International Journal of Climatology 27:1467–1477.CrossRefGoogle Scholar
Brown, S. C., Keatinge, J. D. H., Gregory, P. J. and Cooper, P. J. M. (1987). Effects of fertilizer, variety, and location on barley production under rainfed conditions in Northern Syria. 1. Root and shoot growth. Field Crops Research 16:53–66.CrossRefGoogle Scholar
Cossani, C. M., Savin, R. and Slafer, G. A. (2007). Contrasting performance of barley and wheat in a wide range of conditions in Mediterranean Catalonia (Spain). Annals of Applied Biology 151:167–173.CrossRefGoogle Scholar
Delbecque, N. (2010). Effect of Weed Infestation on Barley Production in Tigray (Northern Ethiopia) and Its Modeling in AquaCrop. Unpub. MSc thesis, Department of Earth and Environmental Sciences, K.U. Leuven, Leuven, Belgium, 113 pp.Google Scholar
EcoPort (2010). EcoPort, the Consilience Engine. Available from: http://ecoport.org/ (accessed 1 January 2011).Google Scholar
FAOSTAT (2010). FAO Statistical Data. Available from: http://faostat.fao.org/site/339/default.aspx (accessed 1 January 2011).Google Scholar
Fischer, R. A. and Maurer, R. (1978). Drought resistance in spring wheat cultivars. I. Grain yield responses. Australian Journal of Agricultural Research 29:897–912.CrossRefGoogle Scholar
Harlan, H. V. and Martini, L. M. (1936). Problems and results in barley breeding. In USDA Yearbook of Agriculture 1936:303-346. Washington, DC: USDA.Google Scholar
Heyland, K.-U. and Werner, A. (2002). Barley (Hordeum vulgare L.). Lehrstuhl fuer Speziellen Pflanzenbau und Pflanzenzuechtung, University of Bonn, German, 7 pp.Google Scholar
Hsiao, T. C., Heng, L., Steduto, P., Rogas-Lana, B., Raes, D. and Fereres, E. (2009). AquaCrop: the FAO crop water model to simulate yield response to water: III. Parameterization and testing for maize. Agricultural Journal 101:448–459.Google Scholar
Jamieson, P. D., Martin, R. J. and Francis, G. S. (1995). Drought influences on grain yield of barley, wheat, and maize. New Zealand Journal of Crop and Horticultural Science 23:55–66.CrossRefGoogle Scholar
Kipkorir, E. C., Raes, D., Bargerei, R. G. and Mugalavai, E. M. (2007). Evaluation of two risk assessment methods for sowing maize in Kenya. Agricultural and Forest Meteorology 144:193–199.CrossRefGoogle Scholar
Krause, P., Boyle, D. P. and Base, F. (2005). Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences 5:89–97.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
Matar, A. E. and Brown, S. C. (1989). Effect of rate and method of phosphate placement on productivity of durum wheat in a Mediterranean climate. Fertilizer Research 20:83–88.CrossRefGoogle Scholar
Nash, J. E. and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. I. A discussion of principles. Journal of Hydrology 10:282–290.CrossRefGoogle Scholar
Nyssen, J., Covaerts, B., Araya, T., Cornelis, W., Bauer, H., Haile, M., Sayre, K. and Deckers, J. 2011. The use of the marasha ard plough for conservation agriculture, Agronomy for Sustainable Development 31:287–297. Available at: http://www.agronomy-journal.org/10.1051/agro/2010014 (Accessed 25 March 2011).CrossRefGoogle Scholar
Raes, D., Sithole, A., Makarau, A. and Milfod, J. (2004). Evaluation of first planting dates recommended by criteria currently used in Zimbabwe. Agricultural and forest meteorology 125:177–185.CrossRefGoogle Scholar
Raes, D., Steduto, P., Hsiao, T. C. and Fereres, E. (2009). AquaCrop: the FAO crop model to simulate yield response to water: II. Main Algorithms and software description. Agricultural Journal 101:438–447.Google Scholar
Raes, D., Steduto, P., Hsiao, T. C. and Fereres, E. (2010). Reference manual Aquacrop (Version 3.1). Rome, Italy: Land and Water Development Division, FAO, 89 pp.Google Scholar
Raes, D., Willems, P. and Gbaguidi, F. (2006). Rainbow Version 2.2. A Software Package for Analyzing Climatological/Hydrological Data Frequency Analysis – Test of Homogeneity. Interuniversity Programme in Water Resources Engineering, K.U. Leuven University – Vrije Universiteit Brussel, IUPWARE, Belgium.Google Scholar
Reggers, R. (2008). Yield Assessment for Common Cereals in the Highlands of Tigray. Unpub. MSc thesis, Department of Land Management, K.U. Leuven, Leuven, Belgium, 133 pp.Google Scholar
Ritchie, J. T. (1972). Model for predicting evaporation from a row crop with incomplete cover. Water Resources Research 8:1204–1213.CrossRefGoogle 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
SPSS Inc. (1988). Sigma Scan Pro 5.0: Image Analysis. Chicago, IL: SPSS Science Marketing Department.Google Scholar
Steduto, P., Hsiao, T. C., Raes, D. and Fereres, E. (2009). AquaCrop: the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agricultural. Journal 101:426–437.Google Scholar
Tilahun, K. (2006). Analysis of rainfall climate and evapo-transpiration in arid and semi-arid regions of Ethiopia using data over the last half a century. Journal of Arid Environments. 64:474–487.CrossRefGoogle Scholar
Vanuytrecht, E. (2007). Crop Water Productivity of Barley and Tef in the Highlands of Tigray (Northern Ethiopia). Unpub. MSc thesis, Department of Land Management, K.U. Leuven, Leuven, Belgium, 144 pp.Google Scholar
Viaene, N. (2009). Analysis of the Effect of Water and Fertility Stress on the Production of Tef and Barley in Tigray (Ethiopia) by AquaCrop. Unpub. MSc thesis, Department of Earth and Environmental Sciences, K.U. Leuven, Leuven, Belgium, 102 pp.Google Scholar
Von Braun, J. (1991). A policy agenda for famine prevention in Africa. Food Policy Statement No. 13. Washington, DC: IFPRI.Google Scholar
Wahbi, A. and Sinclair, T. R. (2005). Simulation analysis of relative yield advantage of barley and wheat in an eastern Mediterranean climate. Field Crops Research 91:287–296.CrossRefGoogle Scholar