Validating the AquaCrop model for maize under different sowing dates

Water Policy ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 826-840 ◽  
Author(s):  
Waseem Raja ◽  
Raihana Habib Kanth ◽  
Purshotum Singh

Abstract The crop growth simulation driven by daily climatic data can be used to predict the yield under varied climatic conditions. The simulation model can be exploited to reduce production risk and to evaluate the effect of soil, water, field management and climate on crop production. In this study, the FAO AquaCrop model was calibrated and validated for maize under varied sowing dates during 2012 and 2013. The experiment was conducted at Shalimar Campus of Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir during kharif seasons of 2012 and 2013. Calibration was done using data of 28 April sowing of 2012 and validation was done by using data of 10 May and 25 May sowing of 2012, and 28 April, 10 May and 25 May sowing of 2013. The simulated grain yield and crop water use was acceptable as root mean square error (RMSE) between simulated and measured observations was low (≤0.05 t ha−1 and ≤16.72 mm) with high coefficient of efficiency (0.99 and 0.71), respectively. The model also performed satisfactorily for the canopy cover and in-season biomass under varied sowing dates having RMSE ≤9.97% and ≤1.78 t ha−1, respectively.

2021 ◽  
Vol 23 (1) ◽  
pp. 122-126
Author(s):  
MAHESH CHAND SINGH ◽  
VAJINDER PAL ◽  
SOM PAL SINGH ◽  
SANJAY SATPUTE

Climate change which is one of the main determinants of agricultural production has started affecting the crop growth pattern and yield from past couple of decades in various agro-climatic zones globally. Under such scenario, the prior forecasting of yield of field crops such as wheat via modeling techniques can help in simplifying the crop production management system starting from farmer’s level to policy makers. The present study was thus undertaken to model the wheat yield of Ludhiana district of  Indian Punjab through regression analysis of historical data (1993-2017) of wheat yield and climatic conditions in the area. The developed model was successfully validated with a strong positive correlation (R2=0.81) between predicted and observed data. Both observed and predicted yields were having similar trend with a minimum and maximum absolute differential error of 0.1 and 13.9% respectively. The developed model may serve as a powerful tool for predicting the future yield of wheat crop with available futuristic climatic data of the study area.


2014 ◽  
Vol 153 (7) ◽  
pp. 1218-1233 ◽  
Author(s):  
H. VAN GAELEN ◽  
A. TSEGAY ◽  
N. DELBECQUE ◽  
N. SHRESTHA ◽  
M. GARCIA ◽  
...  

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.


2019 ◽  
Vol 125 (3) ◽  
pp. 433-445 ◽  
Author(s):  
Urs K Weber ◽  
Scott L Nuismer ◽  
Anahí Espíndola

Abstract Background and Aims The diversity of floral morphology among plant species has long captured the interest of biologists and led to the development of a number of explanatory theories. Floral morphology varies substantially within species, and the mechanisms maintaining this diversity are diverse. One possibility is that spatial variation in the pollinator fauna drives the evolution of spatially divergent floral ecotypes adapted to the local suite of pollinators. Another possibility is that geographic variation in the abiotic environment and local climatic conditions favours different floral morphologies in different regions. Although both possibilities have been shown to explain floral variation in some cases, they have rarely been competed against one another using data collected from large spatial scales. In this study, we assess floral variation in relation to climate and floral visitors in four oil-reward-specialized pollination interactions. Methods We used a combination of large-scale plant and pollinator samplings, morphological measures and climatic data. We analysed the data using spatial approaches, as well as traditional multivariate and structural equation modelling approaches. Key Results Our results indicate that the four species have different levels of specialization, and that this can be explained by their climatic niche breadth. In addition, our results show that, at least for some species, floral morphology can be explained by the identity of floral visitors, with climate having only an indirect effect. Conclusions Our results demonstrate that, even in very specialized interactions, both biotic and abiotic variables can explain a substantial amount of intraspecific variation in floral morphology.


2021 ◽  
Vol 70 (1) ◽  
pp. 41-59
Author(s):  
Ružica Stričević ◽  
Mirjam Vujadinović-Mandić ◽  
Nevenka Đurović ◽  
Aleksa Lipovac

Frequent occurrence of droughts over the last two decades, as well as increases in the air temperature increase have led to the rise farmers' concerns that field crop production would not be possible without irrigation. The aim of this research is to assess how two adaptation measures, sowing dates and irrigation and water excess impacts the yields of wheat, maize and sunflower in Serbia. In order to assess the future of climatic condition five representative locations have been selected for the analysis (Novi Sad, Valjevo, Kragujevac, Negotic and Leskovac). For the analysis of future climatic conditions, results of the ensemble of nine regional climate models from the Euro-CORDEX database were used. The period between 1986 and 2005 was used as a reference, while time slices in the future are: 2016-2035 (near future), 2046-2065 (mid-century) and 2081-2100 (end of the century). Analyses were made for the scenario of GHG emmisions RCP8.5. Aquacrop model v.6.1 was used for the yield, sowing period, and irrigation requirement assessment. The analysis and the results have indicated that earlier start of the growing season of maize and sunflower for 5, 11 and 19 days in near future, mid and end of the century, respectively, whereas optimal sowing period for rainfed wheat will vary from September 20 to November 30, depending on rainfall occurrence, and for irrigated one in optimal sowing period (beginning of October). The warmer climate will shorten the growing cycle of all studied crops. However, the shortening significantly differs among locations. The growing cycle of maize shortened from 34 up to 48 days in Valjevo in near future through the end of the century, while in Negotin it could be less only for 6 days. The increase in air temperature and earlier start of the growing season will enable the most sensitive phenophases, flowering and fruit formation, to appear in a period of more favorable weather conditions, together with the increase in CO2 concentration, can help mitigate the negative impact of the climate change, so that there will be no reduction in sunflower yields. Slight increment of sunflower yields could be expected by the end of century (2.3 - 13.8%), whereas yield of maize will remain on the present level. The increase of wheat yield could be expected only in the near future (up to 8.3 %), but also it can be reduced at some locations by the end of the century. Irrigation water requirements of all studied crops will remain at the same level the same level as the present, but only if sowing applied in the optimal period. Although it is known that irrigation changes microclimatic conditions, ie., the air humidity increases, and the air temperature decreases (the so-called oasis effect), which can affect the extension of the vegetation period, and thus the increase in yield. Such subtle changes in the microclimate cannot be "recognized" by models, so even simulated yields cannot be fully (accurately) predicted. This research come to the conclusion that in addition to irrigation, shifting the sowing dates earlier can have an impact on mitigating the consequences of climate change in crop production, which is of great importance for areas where there is not enough water for irrigation. The risk of drought will exist on shallow and sandy soils as well as on overwetted lands that cannot be plowed until drained to be sown in optimal terms and all crops sown in the late spring.


Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1976
Author(s):  
Muhammad Mehran Anjum ◽  
Muhammad Arif ◽  
Muhammad Riaz ◽  
Kashif Akhtar ◽  
Sheng Quan Zhang ◽  
...  

Predicted decrease in water availability for crop production and uncertainty in climatic conditions require devising the irrigation strategies to increase water use efficiency (WUE) for sustainable crop production. The development of crop cultivars with higher WUE is a pre-requisite for such strategies, particularly in developing countries, including Pakistan, who face stern food security challenges. A two-year field study was conducted following a split-plot randomized complete block design to understand the effects of wheat cultivars (hybrid cultivars, 18A-1 and 18A-2, and local cultivar Ghaneemat IBGE-2016), sowing dates (15th November, 30th November, and 15th December), and irrigation regimes [I (103 mm), II (175 mm), III (254 mm), and IV (330 mm) at four different growth stages of tillering, booting, anthesis and grain filling on wheat productivity, biomass production and grain yield, and crop-water relations. Early sown hybrid cultivars 18A-1 and 18A-2 showed significantly higher biological and grain yields compared to the local cultivar (59% and 69% higher than the local cultivar). Trends in biomass production and grain yield were also similar at later sowing dates of 30th November and 15th December. However, biological and grain yields decreased with delay in sowing for each cultivar. The data also revealed that hybrid cultivars were better suited to deficient irrigation and generally produced significantly higher biological and grain yields under each moisture regime. Cultivars, sowing dates, and irrigation regime differed significantly for their effects on the Soil Plant Analysis Development (SPAD) values, chlorophyll a and b contents but not for carotenoids. Sowing dates and irrigation regimes had significant effects on relative water content (RWC), water saturation deficit (WSD), water uptake capacity (WUC), and water retention capacity (WRC); however, only WUC varied significantly between the cultivars. The phenological data show that hybrid cultivars took more days to maturity and grain filling than the local cultivar, and days decreased with delayed sowing. The biological and grain yields show significant positive correlations with SPAD values (p < 0.001) and days to maturity (p < 0.001). Our study shows that hybrid wheat cultivars can be opted for higher biomass production and grain yields under deficit irrigation scenarios of semi-arid climatic conditions in Pakistan. Moreover, the hybrid wheat cultivars can perform better than the indigenous cultivar even for delayed sowing dates of 30th November and 15th December.


2013 ◽  
Vol 67 (1) ◽  
pp. 232-238 ◽  
Author(s):  
Mojtaba Khoshravesh ◽  
Behrouz Mostafazadeh-Fard ◽  
Manouchehr Heidarpour ◽  
Ali-Reza Kiani

On a global scale, irrigated agriculture consumes about 72% of available freshwater resources. Deficit irrigation can be applied in the field to save irrigation water and still lead to acceptable crop production. The AquaCrop model is a simulation model for management of irrigation and nitrogen fertilizer. This model is a new model that is accurate, robust and requires fewer data inputs compared with the other models. The purpose of this study was to simulate canopy cover, grain yield and water use efficiency (WUE) for soybean using the AquaCrop model. A field line source sprinkler irrigation system was conducted under full and deficit irrigation using different nitrogen fertilizer applications during two cropping seasons for soybean at Gorgan province in Iran. The simulation results showed a reasonably accurate prediction of yield, canopy cover and WUE in all cases (error less than 23%). The simulated pattern of canopy progression over time was close to measured values, with Willmott's index of agreement for all the cases being ≥0.95 for different parameters. The AquaCrop model has the ability to simulate the WUE of soybean under different irrigation water and nitrogen applications. This model is a useful tool for managing the crop water productivity.


2020 ◽  
Vol 4 ◽  
Author(s):  
Erika A. Warnatzsch ◽  
David S. Reay ◽  
Marco Camardo Leggieri ◽  
Paola Battilani

Malawi is one of the poorest countries in the world, with high levels of malnutrition and little domestic mycotoxin regulation. Domestically grown maize is the largest single source of calories in the country and a large contributor to the economy. This research uses Regional Climate Models (RCMs) to determine the climatic conditions in the three regions of Malawi (Northern, Central and Southern) in 2035 (2020–2049) and 2055 (2040–2069) as compared to the baseline climate of 1971–2000. This climatic data is then used as inputs to the Food and Agriculture Organization's (FAO) AquaCrop model to assess the impact on the growth cycle of two maize varieties grown in each region and sown at three different times during the planting season. Finally, AFLA-maize, a mechanistic model, is applied to determine the impact of these projected changes on the aflatoxin B1 (AFB1) contamination risk. We find that Malawi's climate is projected to get warmer (by 1–2.5°C) and drier (reduction of 0–4% in annual rainfall levels) in all regions, although some uncertainty remains around the changes in precipitation levels. These climatic changes are expected to shorten the growing season for maize, bringing the harvest date forward by between 10 and 25 days for the short-development variety and between 25 and 65 days for the long-development variety. These changes are also projected to make the pre-harvest conditions for Malawian maize more favorable for AFB1 contamination and risk maps for the studied conditions were drawn. Exceedances of EU safety thresholds are expected to be possible in all regions, with the risk of contamination moving northwards in a warming climate.


Author(s):  
Sujata Mulik

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 


Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 927
Author(s):  
Jamshad Hussain ◽  
Tasneem Khaliq ◽  
Muhammad Habib ur Rahman ◽  
Asmat Ullah ◽  
Ishfaq Ahmed ◽  
...  

Rising temperature from climate change is the most threatening factor worldwide for crop production. Sustainable wheat production is a challenge due to climate change and variability, which is ultimately a serious threat to food security in Pakistan. A series of field experiments were conducted during seasons 2013–2014 and 2014–2015 in the semi-arid (Faisalabad) and arid (Layyah) regions of Punjab-Pakistan. Three spring wheat genotypes were evaluated under eleven sowing dates from 16 October to 16 March, with an interval of 14–16 days in the two regions. Data for the model calibration and evaluation were collected from field experiments following the standard procedures and protocols. The grain yield under future climate scenarios was simulated by using a well-calibrated CERES-wheat model included in DSSAT v4.7. Future (2051–2100) and baseline (1980–2015) climatic data were simulated using 29 global circulation models (GCMs) under representative concentration pathway (RCP) 8.5. These GCMs were distributed among five quadrants of climatic conditions (Hot/Wet, Hot/Dry, Cool/Dry, Cool/Wet, and Middle) by a stretched distribution approach based on temperature and rainfall change. A maximum of ten GCMs predicted the chances of Middle climatic conditions during the second half of the century (2051–2100). The average temperature during the wheat season in a semi-arid region and arid region would increase by 3.52 °C and 3.84 °C, respectively, under Middle climatic conditions using the RCP 8.5 scenario during the second half-century. The simulated grain yield was reduced by 23.5% in the semi-arid region and 35.45% in the arid region under Middle climatic conditions (scenario). Mean seasonal temperature (MST) of sowing dates ranged from 16 to 27.3 °C, while the mean temperature from the heading to maturity (MTHM) stage was varying between 12.9 to 30.4 °C. Coefficients of determination (R2) between wheat morphology parameters and temperature were highly significant, with a range of 0.84–0.96. Impacts of temperature on wheat sown on 15 March were found to be as severe as to exterminate the crop before heading. The spikes and spikelets were not formed under a mean seasonal temperature higher than 25.5 °C. In a nutshell, elevated temperature (3–4 °C) till the end-century can reduce grain yield by about 30% in semi-arid and arid regions of Pakistan. These findings are crucial for growers and especially for policymakers to decide on sustainable wheat production for food security in the region.


Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 458
Author(s):  
Tara A. Ippolito ◽  
Jeffrey E. Herrick ◽  
Ekwe L. Dossa ◽  
Maman Garba ◽  
Mamadou Ouattara ◽  
...  

Smallholder agriculture is a major source of income and food for developing nations. With more frequent drought and increasing scarcity of arable land, more accurate land-use planning tools are needed to allocate land resources to support regional agricultural activity. To address this need, we created Land Capability Classification (LCC) system maps using data from two digital soil maps, which were compared with measurements from 1305 field sites in the Dosso region of Niger. Based on these, we developed 250 m gridded maps of LCC values across the region. Across the region, land is severely limited for agricultural use because of low available water-holding capacity (AWC) that limits dry season agricultural potential, especially without irrigation, and requires more frequent irrigation where supplemental water is available. If the AWC limitation is removed in the LCC algorithm (i.e., simulating the use of sufficient irrigation or a much higher and more evenly distributed rainfall), the dominant limitations become less severe and more spatially varied. Finally, we used additional soil fertility data from the field samples to illustrate the value of collecting contemporary data for dynamic soil properties that are critical for crop production, including soil organic carbon, phosphorus and nitrogen.


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