AQUACROP MODEL AS A TOOL TO DISCLOSE THE WATER PRODUCTIVITY OF BAMBARA GROUNDNUT LANDRACES FOR RAINFED FARMING IN BOTSWANA

2013 ◽  
pp. 401-406 ◽  
Author(s):  
A.S. Karunaratne ◽  
S. Azam-Ali
Keyword(s):  
Water Productivity ◽  
Bambara Groundnut ◽  
Rainfed Farming ◽  
Aquacrop Model

CALIBRATION AND VALIDATION OF FAO-AQUACROP MODEL FOR IRRIGATED AND WATER DEFICIENT BAMBARA GROUNDNUT

2011 ◽  
Vol 47 (3) ◽  
pp. 509-527 ◽  
Author(s):  
A. S. KARUNARATNE ◽  
S. N. AZAM-ALI ◽  
G. IZZI ◽  
P. STEDUTO
Keyword(s):  
Field Experiments ◽  
Water Productivity ◽  
Canopy Cover ◽  
Research Unit ◽  
Bambara Groundnut ◽  
Women Farmers ◽  
Sub Saharan Africa ◽  
Yield Response ◽  
Grain Legume ◽  
Aquacrop Model

SUMMARYSimulation of yield response to water plays an increasingly important role in optimization of crop water productivity (WP) especially in prevalent drought in Africa. The present study is focused on a representative crop: bambara groundnut (Vigna subterranea), an ancient grain legume grown, cooked, processed and traded mainly by subsistence women farmers in sub-Saharan Africa. Over four years (2002, 2006–2008), glasshouse experiments were conducted at the Tropical Crops Research Unit, University of Nottingham, UK under controlled environments with different landraces, temperatures (23 ± 5 °C, 28 ± 5 °C, 33 ± 5 °C) and soil moisture regimes (irrigated, early drought, late drought). Parallel to this, field experiments were conducted in Swaziland (2002/2003) and Botswana (2007/2008). Crop measurements of canopy cover (CC), biomass (B) and pod yield (Y) of selected experiments from glasshouse (2006 and 2007) and field (Botswana) were used to calibrate the FAO AquaCrop model. Subsequently, the model was validated against independent data sets from glasshouse (2002 and 2008) and field (Swaziland) for different landraces. AquaCrop simulations for CC, B and Y of different bambara groundnut landraces are in good agreement with observed data with R2 (CC-0.88; B-0.78; Y-0.72), but with significant underestimation for some landraces.

scholarly journals A semi-quantitative approach for modelling crop response to soil fertility: evaluation of the AquaCrop procedure

2014 ◽  
Vol 153 (7) ◽  
pp. 1218-1233 ◽  
Author(s):  
H. VAN GAELEN ◽  
A. TSEGAY ◽  
N. DELBECQUE ◽  
N. SHRESTHA ◽  
M. GARCIA ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Grain Yield ◽  
Soil Water ◽  
Crop Production ◽  
Water Productivity ◽  
Canopy Cover ◽  
Quantitative Approach ◽  
Fertility Level ◽  
Crop Response ◽  
Aquacrop Model

SUMMARYMost crop models make use of a nutrient-balance approach for modelling crop response to soil fertility. To counter the vast input data requirements that are typical of these models, the crop water productivity model AquaCrop adopts a semi-quantitative approach. Instead of providing nutrient levels, users of the model provide the soil fertility level as a model input. This level is expressed in terms of the expected impact on crop biomass production, which can be observed in the field or obtained from statistics of agricultural production. The present study is the first to describe extensively, and to calibrate and evaluate, the semi-quantitative approach of the AquaCrop model, which simulates the effect of soil fertility stress on crop production as a combination of slower canopy expansion, reduced maximum canopy cover, early decline in canopy cover and lower biomass water productivity. AquaCrop's fertility response algorithms are evaluated here against field experiments with tef (Eragrostis tef (Zucc.) Trotter) in Ethiopia, with maize (Zea mays L.) and wheat (Triticum aestivum L.) in Nepal, and with quinoa (Chenopodium quinoa Willd.) in Bolivia. It is demonstrated that AquaCrop is able to simulate the soil water content in the root zone, and the crop's canopy development, dry above-ground biomass development, final biomass and grain yield, under different soil fertility levels, for all four crops. Under combined soil water stress and soil fertility stress, the model predicts final grain yield with a relative root-mean-square error of only 11–13% for maize, wheat and quinoa, and 34% for tef. The present study shows that the semi-quantitative soil fertility approach of the AquaCrop model performs well and that the model can be applied, after case-specific calibration, to the simulation of crop production under different levels of soil fertility stress for various environmental conditions, without requiring detailed field observations on soil nutrient content.

EVALUATION OF AQUACROP MODEL IN SIMULATING BITTER GOURD WATER PRODUCTIVITY UNDER SALINE IRRIGATION

2019 ◽  
Vol 69 (1) ◽  
pp. 63-73
Author(s):  
Kamran Baksh Soomro ◽  
Sina Alaghmand ◽  
Muhammad Rizwan Shahid ◽  
Sanyogita Andriyas ◽  
Amin Talei
Keyword(s):  
Water Productivity ◽  
Bitter Gourd ◽  
Saline Irrigation ◽  
Aquacrop Model

Improving Dryland Crop Simulation by Assimilating Soil Moisture and Vegetation Data

2020 ◽  
Author(s):  
Yang Lu ◽  
Justin Sheffield
Keyword(s):  
Soil Moisture ◽  
Crop Production ◽  
Management Practices ◽  
Water Productivity ◽  
Vegetation Indices ◽  
Screening Method ◽  
Model Performance ◽  
State Parameter ◽  
Aquacrop Model

<p>Global population is projected to keep increasing rapidly in the next 3 decades, particularly in dryland regions of the developing world, making it a global imperative to enhance crop production. However, improving current crop production in these regions is hampered by yield gaps due to poor soils, lack of irrigation and other management practices. Here we develop a crop modelling capability to help understand gaps, and apply to dryland regions where data for parametrizing and testing models is generally lacking. We present a data assimilation framework to improve simulation capability by assimilating in-situ soil moisture and vegetation data into the FAO AquaCrop model. AquaCrop is a water-driven model that simulates canopy growth, biomass and crop yield as a function of water productivity. The key strength of AquaCrop lies in the low requirement for input data thanks to its simple structure. A global sensitivity analysis is first performed using the Morris screening method and the variance-based Extended Fourier Amplitude Sensitivity Test (EFAST) method to identify the key influential parameters on the model outputs. We begin with state-only updates by assimilating different combinations of soil moisture and vegetation data (vegetation indices, biomass, etc.), and different filtering/smoothing assimilation strategies are tested. Based on the state-only assimilation results, we further evaluate the utility of joint state-parameter (augmented-states) assimilation in improving the model performance. The framework will eventually be extended to assimilate remote sensing estimates of soil moisture and vegetation data to overcome the lack of in-situ data more generally in dryland regions.</p>

scholarly journals Adapatation of Sowing Date for Improving Sorghum Yield in Rainfed Areas in Sudan Using AQUACROP Model

2017 ◽  
Vol 9 (8) ◽  
pp. 220
Author(s):  
Mohammed Abd Almahamoud Alshikh ◽  
Hassn Ibrahim M. ◽  
Salah Abdel Rahman Salih ◽  
Ali Hussien Kadhim ◽  
Khalid Abd Almageed M.
Keyword(s):  
Water Resources ◽  
Crop Production ◽  
Spatial Information ◽  
Water Productivity ◽  
Sowing Date ◽  
Future Research ◽  
Soil Conditions ◽  
World Population ◽  
Aquacrop Model ◽  
Rain Fall

Due to the rapid growth in world population, the pressure on water resources to feed the growing population is increasing. The Nile water share of Sudan is almost exploited; and agricultural production by rained water is threatened by the pressure of climate change. It is inevitable that the production per unit water consumed, the water productivity, must be increased to meet this challenge. This research therefore focuses on the benchmarking of physical water productivity in rain fed areas and gaining a better understanding of the temporal and spatial variations and the scope for possible improvement. A review of the available records and sources that provide measurements of crop-water productivity was consulted to assess plausible ranges of water productivity levels for rain fed Sorghum crop and to provide a first explanation for the differences that are found using AQUACROP model. As such this study may be considered as crucial step was to establish a water productivity database for the rain fed sorghum crop in the country. Sorghum (Sorghum bicolor (L.) Moench) which is the most important cereal crop in Sudan has been constrained by the detrimental effect of drought which has often caused food shortages. Almost 90% of the total sorghum cropped area is rain-fed, and 60% of that is in drought prone soil conditions. Spatial information on water use, crop production and water productivity will play a vital role for water managers to assess where scarce water resources are wasted and where in a given region the water productivity can be improved. Hence, a methodology has been developed in this study to quantify spatial variation of crop yield, evapotranspiration and water productivity using the AQUACROP model in five stations. The AQUACROP model is used to investigate optimum sowing date that result in maximization of grain yield.Benchmarking of rain fed Sorghum actual and potential grain efficiency in different agro-climate zones was made for the year 1979 to 2013. AQUACROP model was applied at five locations (Gedaref, Damazin, Dalang, El Fashir, and El Obyied) each representing an agro-climate zone. Causes of poor yield performance were investigated and consequently measures needed to improve performance were identified. The study indicates that increase in sorghum yields under historical climate conditions in the different studied stations is possible when early sowing is used and initial rain showers are utilized, yield decrease by 43% when sowing date is delayed from July 15 (the recommended date) to August 1. Stations with high rain fall (Damazin, Gadaref and Dalang) show little variations in inter-annual yields but with a tendency towards high yields, 3536, 3741, 3737 kg/fed for the above stations respectively compared to 2266 and 1086 kg/fed for El Obyied and El Fashir respectively at 15 June. The obtained WUE is lower in the driest regions (El Fashir, and El Obyied) and higher for those of high rain fall. To aid decision makers and crop growers in rain fed areas a set of recommendations for policy making and for future research were identified.

Modeling effects of plastic mulching on crop yields and water productivity in the middle Heihe River Basin

2020 ◽  
Author(s):  
Wang Zhang ◽  
Chunmiao Zheng
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Global Sensitivity Analysis ◽  
Water Productivity ◽  
Regional Scale ◽  
Canopy Cover ◽  
Heihe River ◽  
Aquacrop Model ◽  
Plastic Mulching

<p>Plastic mulching is an effective field practice to improve crop water productivity (WP), especially widely used in arid and semi-arid areas. The positive effects of soil mulching on crop yield and WP have been studied through numerous field experiments and simulations at the site scale. However, few studies have focused on the mulching effects at the regional scale. Zhangye oasis, a typical arid region in the middle Heihe River Basin, was chosen as the study area. Global sensitivity analysis was applied to determine the most sensitive parameters in AquaCrop model. Based on the results of global sensitivity analysis, soil and crop parameters of AquaCrop model were calibrated and validated using field observations from three stations. The normalized root mean square error (NRMSE) values for soil water content, seed maize canopy cover, aboveground biomass, yield, spring wheat canopy cover, aboveground biomass and yield were 18.7%, 6.7%, 23.5%, 12.5%, 10.7%, 24.2% and 15.0% during the calibration period, and the corresponding values during the validation period were 25.1%, 7.0%, 22.2%, 17.7%, 9.1%, 23.6% and 11.7%, respectively. These values indicated the calibrated model performed well to simulate the soil water content and crop growth. Compared with no-mulching, the average soil water content during the growth period, seed maize yield and WP under mulching had been increased by 8.8%, 3.0% and 3.0%, respectively. The results demonstrated that plastic mulching could effectively improve the yield and WP of seed maize, which not significantly on spring wheat. This study offers a quantitatively analysis for plastic mulching applications at the regional scale.</p>

scholarly journals Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria

2018 ◽  
Vol 156 (5) ◽  
pp. 658-672 ◽  
Author(s):  
B. Lalić ◽  
A. Firanj Sremac ◽  
J. Eitzinger ◽  
R. Stričević ◽  
S. Thaler ◽  
...  
Keyword(s):  
Crop Yield ◽  
Spring Barley ◽  
Water Productivity ◽  
Weather Forecast ◽  
Seasonal Forecasting ◽  
Green Water ◽  
Weather Data ◽  
Medium Range Weather Forecast ◽  
Probabilistic Forecasts ◽  
Aquacrop Model

AbstractA probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

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