Search results for: AquaCrop

Authors: Yenesew Mengiste Yihun, Abraham Mehari Haile, Teklu Erkossa, Bart Schultz

Abstract:

Teff (Eragrostic tef) is a staple food in Ethiopia. The local and international demand for the crop is ever increasing pushing the current price five times compared with that in 2006. To meet this escalating demand increasing production including using irrigation is imperative. Optimum application of irrigation water, especially in semi-arid areas is profoundly important. AquaCrop model application in irrigation water scheduling and simulation of water productivity helps both irrigation planners and agricultural water managers. This paper presents simulation and evaluation of AquaCrop model in optimizing the yield and biomass response to variation in timing and rate of irrigation water application. Canopy expansion, canopy senescence and harvest index are the key physiological processes sensitive to water stress. For full irrigation water application treatment there was a strong relationship between the measured and simulated canopy and biomass with r2 and d values of 0.87 and 0.96 for canopy and 0.97 and 0.74 for biomass, respectively. However, the model under estimated the simulated yield and biomass for higher water stress level. For treatment receiving full irrigation the harvest index value obtained were 29%. The harvest index value shows generally a decreasing trend under water stress condition. AquaCrop model calibration and validation using the dry season field experiments of 2010/2011 and 2011/2012 shows that AquaCrop adequately simulated the yield response to different irrigation water scenarios. We conclude that the AquaCrop model can be used in irrigation water scheduling and optimizing water productivity of Teff grown under water scarce semi-arid conditions.

Keywords: AquaCrop, climate smart agriculture, simulation, teff, water security, water stress regions

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Authors: Marilyn S. Painagan

Abstract:

This general objective of the study was to apply the AquaCrop model to the conditions in the municipality of Carmen, North Cotabato in terms of predicting corn yields in this area and determine the influence of rainfall and soil depth on simulated yield. The study revealed wide disparity in monthly yields as a consequence of similarly varying monthly rainfall magnitudes. It also found out that simulated yield varies with the depth of soil, which in this case was clay loam, the predominant soil in the study area. The model was found to be easy to use even with limited data and shows a vast potential for various farming and policy applications, such as formulation of a cropping calendar.

Keywords: aquacrop, evapotranspiration, crop modelling, crop simulation

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Authors: Teklay Tesfay, Gebreyesus Brhane Tesfahunegn, Abadi Berhane, Selemawit Girmay

Abstract:

Adjusting the proper sowing date of a crop at a particular location with a changing climate is an essential management option to maximize crop yield. However, determining the optimum sowing date for rainfed maize production through field experimentation requires repeated trials for many years in different weather conditions and crop management. To avoid such long-term experimentation to determine the optimum sowing date, crop models such as AquaCrop are useful. Therefore, the overall objective of this study was to evaluate the performance of AquaCrop model in simulating maize productivity under varying sowing dates. A field experiment was conducted for two consecutive cropping seasons by deploying four maize seed sowing dates in a randomized complete block design with three replications. Input data required to run this model are stored as climate, crop, soil, and management files in the AquaCrop database and adjusted through the user interface. Observed data from separate field experiments was used to calibrate and validate the model. AquaCrop model was validated for its performance in simulating the green canopy and aboveground biomass of maize for the varying sowing dates based on the calibrated parameters. Results of the present study showed that there was a good agreement (an overall R2 =, Ef= d= RMSE =) between measured and simulated values of the canopy cover and biomass yields. Considering the overall values of the statistical test indicators, the performance of the model to predict maize growth and biomass yield was successful, and so this is a valuable tool help for decision-making. Hence, this calibrated and validated model is suggested to use for determining optimum maize crop sowing date for similar climate and soil conditions to the study area, instead of conducting long-term experimentation.

Keywords: AquaCrop model, calibration, validation, simulation

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Authors: Abadi Berhane, Walelign Worku, Berhanu Abrha, Gebre Hadgu, Tigray

Abstract:

Sesame is an important oilseed crop in Ethiopia; which is the second most exported agricultural commodity next to coffee. However, there is poor soil fertility management and a research-led farming system for the crop. The AquaCrop model was applied as a decision-support tool; which performs a semi-quantitative approach to simulate the yield of crops under different soil fertility levels. The objective of this experiment was to calibrate and validated the AquaCrop model for simulating the growth and yield of sesame under different nitrogen fertilizer levels and to test the performance of the model as a decision-support tool for improved sesame cultivation in the study area. The experiment was laid out as a randomized complete block design (RCBD) in a factorial arrangement in the 2016, 2017, and 2018 main cropping seasons. In this experiment, four nitrogen fertilizer rates; 0, 23, 46, and 69 Kg/ha nitrogen, and three improved varieties (Setit-1, Setit-2, and Humera-1). In the meantime, growth, yield, and yield components of sesame were collected from each treatment. Coefficient of determination (R2), Root mean square error (RMSE), Normalized root mean square error (N-RMSE), Model efficiency (E), and Degree of agreement (D) were used to test the performance of the model. The results indicated that the AquaCrop model successfully simulated soil water content with R2 varying from 0.92 to 0.98, RMSE 6.5 to 13.9 mm, E 0.78 to 0.94, and D 0.95 to 0.99; and the corresponding values for AB also varied from 0.92 to 0.98, 0.33 to 0.54 tons/ha, 0.74 to 0.93, and 0.9 to 0.98, respectively. The results on the canopy cover of sesame also showed that the model acceptably simulated canopy cover with R2 varying from 0.95 to 0.99, and a RMSE of 5.3 to 8.6%. The AquaCrop model was appropriately calibrated to simulate soil water content, canopy cover, aboveground biomass, and sesame yield; the results indicated that the model adequately simulated the growth and yield of sesame under the different nitrogen fertilizer levels. The AquaCrop model might be an important tool for improved soil fertility management and yield enhancement strategies of sesame. Hence, the model might be applied as a decision-support tool in soil fertility management in sesame production.

Keywords: aquacrop model, sesame, normalized water productivity, nitrogen fertilizer

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Authors: Abadi Berhane, Walelign Worku, Berhanu Abrha, Gebre Hadgu

Abstract:

Sesame is an important oilseed crop in Ethiopia, which is the second most exported agricultural commodity next to coffee. However, there is poor soil fertility management and a research-led farming system for the crop. The AquaCrop model was applied as a decision-support tool, which performs a semi-quantitative approach to simulate the yield of crops under different soil fertility levels. The objective of this experiment was to calibrate and validate the AquaCrop model for simulating the growth and yield of sesame under different nitrogen fertilizer levels and to test the performance of the model as a decision-support tool for improved sesame cultivation in the study area. The experiment was laid out as a randomized complete block design (RCBD) in a factorial arrangement in the 2016, 2017, and 2018 main cropping seasons. In this experiment, four nitrogen fertilizer rates, 0, 23, 46, and 69 Kg/ha nitrogen, and three improved varieties (Setit-1, Setit-2, and Humera-1). In the meantime, growth, yield, and yield components of sesame were collected from each treatment. Coefficient of determination (R2), Root mean square error (RMSE), Normalized root mean square error (N-RMSE), Model efficiency (E), and Degree of agreement (D) were used to test the performance of the model. The results indicated that the AquaCrop model successfully simulated soil water content with R2 varying from 0.92 to 0.98, RMSE 6.5 to 13.9 mm, E 0.78 to 0.94, and D 0.95 to 0.99, and the corresponding values for AB also varied from 0.92 to 0.98, 0.33 to 0.54 tons/ha, 0.74 to 0.93, and 0.9 to 0.98, respectively. The results on the canopy cover of sesame also showed that the model acceptably simulated canopy cover with R2 varying from 0.95 to 0.99 and a RMSE of 5.3 to 8.6%. The AquaCrop model was appropriately calibrated to simulate soil water content, canopy cover, aboveground biomass, and sesame yield; the results indicated that the model adequately simulated the growth and yield of sesame under the different nitrogen fertilizer levels. The AquaCrop model might be an important tool for improved soil fertility management and yield enhancement strategies of sesame. Hence, the model might be applied as a decision-support tool in soil fertility management in sesame production.

Keywords: aquacrop model, normalized water productivity, nitrogen fertilizer, canopy cover, sesame

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Authors: V. S. Manivasagam, R. Nagarajan

Abstract:

Modeling the soil, water and crop growth interactions are attaining major importance, considering the future climate change and water availability for agriculture to meet the growing food demand. Progress in understanding the crop growth response during water stress period through crop modeling approach provides an opportunity for improving and sustaining the future agriculture water use efficiency. An attempt has been made to evaluate the potential use of crop modeling approach for assessing the minimal supplementary irrigation requirement for crop growth during water limited condition and its practical significance in sustainable improvement of crop yield and water productivity. Among the numerous crop models, water driven-AquaCrop model has been chosen for the present study considering the modeling approach and water stress impact on yield simulation. The study has been evaluated in rainfed maize grown area of semi-arid Shanmuganadi watershed (a tributary of the Cauvery river system) located in southern India during the rabi cropping season (October-February). In addition to actual rainfed maize growth simulation, irrigated maize scenarios were simulated for assessing the supplementary irrigation requirement during water shortage condition for the period 2012-2015. The simulation results for rainfed maize have shown that the average maize yield of 0.5-2 t ha-1 was observed during deficit monsoon season (<350 mm) whereas 5.3 t ha-1 was noticed during sufficient monsoonal period (>350 mm). Scenario results for irrigated maize simulation during deficit monsoonal period has revealed that 150-200 mm of supplementary irrigation has ensured the 5.8 t ha-1 of irrigated maize yield. Thus, study results clearly portrayed that minimal application of supplementary irrigation during the critical growth period along with the deficit rainfall has increased the crop water productivity from 1.07 to 2.59 kg m-3 for major soil types. Overall, AquaCrop is found to be very effective for the sustainable irrigation assessment considering the model simplicity and minimal inputs requirement.

Keywords: AquaCrop, crop modeling, rainfed maize, water stress

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Authors: Marilyn S. Painagan, Willie Jones B. Saliling

Abstract:

This paper reports an optimized cropping calendar and land suitability for maize in North Cotabato derived from modeling crop productivity over time and space. Using Quantum GIS, eight representative soil types and 0.3o x 0.3o climate grids shapefiles were intersected to form thirty two pedoclimatic zones within the boundaries of the province. Surveys were done to ascertain crop performance and phenological properties on field. Based on these surveys, crop parameters were calibrated specific for a variety of maize. Soil properties and climatic data (daily precipitation, maximum and minimum temperatures) from pedoclimatic zones were loaded to the FAO Aquacrop Water Productivity Model along with the crop properties from field surveys to simulate yield from 1980 to 2010. The average yield per month was computed to come up with the month of planting having the highest and lowest probable yield in a year assuming that all lands were planted with maize. The yield attributes were visualized in the Quantum GIS environment. The study revealed that optimal cropping patterns varied across North Cotabato. Highest probable yield (8000 kg/ha) can be obtained when maize is planted on May and September (sandy clay-loam soils) in the northern part of the province while the lowest probable yield (1000 kg/ha) can be obtained when maize is planted on January, February and March (clay loam soils) at the northern part of the province. Yields are simulated on the basis of varieties currently planted by farmers of North Cotabato. The resulting maps suggest where and when maize is most suitable to achieve high yields. There is a need to ground truth and validate the cropping calendar on field.

Keywords: aquacrop, quantum GIS, maize, cropping calendar, water productivity

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Authors: S. M. T. Mustafa, D. Raes, M. Huysmans

Abstract:

Proper utilization of water resource is very important in agro-based Bangladesh. Irrigation schedule based on local environmental conditions, soil type and water availability will allow a sustainable use of water resources in agriculture. In this study, the FAO crop water model (AquaCrop) was used to simulate the different water and fertilizer management strategies in different location of Bangladesh to obtain a management guideline for the farmer. Model was calibrated and validated for wheat (Triticum aestivum L.). The statistical indices between the observed and simulated grain yields obtained were very good with R2, RMSE, and EF values of 0.92, 0.33, and 0.83, respectively for model calibration and 0.92, 0.68 and 0.77, respectively for model validations. Stem elongation (jointing) to booting and flowering stage were identified as most water sensitive for wheat. Deficit irrigation on water sensitive stage could increase the grain yield for increasing soil fertility levels both for loamy and sandy type soils. Deficit irrigation strategies provides higher water productivity than full irrigation strategies and increase the yield stability (reduce the standard deviation). The practical deficit irrigation schedule for wheat for four different stations and two different soils were designed. Farmer can produce more crops by using deficit irrigation schedule under water stress condition. Practical application and validation of proposed strategies will make them more credible.

Keywords: crop-water model, deficit irrigation, irrigation scheduling, wheat

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