Globally, agricultural production activities in coastal cultivation regions (CCRs) are facing challenges under ICV (Mainuddin et al., 2013; IPCC, 2018). These challenges include saline intrusion and freshwater irrigation deficits (CGIAR Research Program on Climate Change, 2016; Abbas and Mayo, 2020). ICV is expected to accelerate the intensity and frequency of adverse environmental factors or “extreme environmental events,” and thus, many aspects of socioeconomic life and, especially, CCRs are threatened (IPCC, 2018; Dang, 2021).
Studies on adaptation solutions to ICV have been conducted around the world (Abbas and Mayo, 2020; Bai and Xiao, 2020). Tesfaye and Seifu (2016) conducted a study on ICV on agricultural activities in Ethiopia. They suggested that key adaptation solutions would involve shifting crop cultivation schedule (CCS). In Ghana, Ndamani and Watanabe (2016) conducted a study on the adaptation solutions to ICV on agriculture and farmers’ perceptions of long-term changes in climate parameters. They found that up to 80% of interviewees reported that a decline in rainfall was a major concern. In Pakistan, Bakhsh and Kamran (2019) investigated the adaptation solution of farmers to ICVs across the semi-arid region of Punjab province. Results stated that farming experience is strongly linked to adaptation solutions to climate variability. In Vietnam, several studies on farmers’ adaptability to ICV have been conducted in the central region of Vietnam by Shrestha et al. (2016) and Thoai et al. (2018), in the Red River Delta by Luu et al. (2019), in the Plain of Reeds by Dang (2021), and in the Mekong Delta by Mishra and Pede (2017) and Lee and Dang (2018). Agricultural activities in CCRs are facing challenges, increasing the risk of a decline in crop yield and even crop failure (Thoai et al., 2018; Dang, 2021).
Kien Giang is one of the provinces facing sea level rise and a decreasing trend in rainfall (Dang et al., 2021; Lee and Dang, 2019). By the middle of the 21st century, the sea level could have risen by 30 cm and rainfall in the dry season could have declined to 10.0 mm. Against the background of irrigation water scarcity, using cultivars with a short life cycle (Dang et al., 2021), shifting CCS (Dang, 2021), and water-saving irrigation (Hasan et al., 2019) are potentially effective adaptation solutions to ICV (Bai and Xiao, 2020). The objective of this study was to evaluate the impacts of rainfall, temperature, and irrigation water in coastal cultivation regions of Kien Giang Province.
Kien Giang is a southwestern coastal province, stretching from 9°23′50″ N–104°26′40″ E to 10°32′30″ N–105°32′40″ E (Figure 1), with the territory varying from 0.2 to 1.0 m above average sea level (IMHEN, Ca Mau Peoples Committee, and Kien Giang Peoples Committee, 2011; Dang et al., 2021). Rice production is one of the strengths of the province, with an estimated total area of agricultural land of up to 350,000 ha (Trang, 2016; Dang et al., 2021). Rice cultivation paddies are supplied with freshwater via irrigation channels and local precipitation (CGIAR Research Program on Climate Change, 2016; Dang et al., 2021).
The area is dominated by two monsoon circulations including the southwest monsoon that lasts from May to October and the northeast monsoon activities from November to April (IMHEN, Ca Mau Peoples Committee, and Kien Giang Peoples Committee, 2011; Dang et al., 2021). The tropical climate has an average temperature of approximately 27.5°C, humidity of 82%, and rainfall ranging from 2200 to 2400 mm (Figure 2). These climatic conditions are highly suitable for agricultural activities (Kontgis et al., 2019; Nguyen et al., 2022).
In CCRs of Kien Giang Province, farmers sow single or double crops each year, except in a small part of the U Minh Thuong region and Vinh Thuan districts that are triple cropped (Kontgis et al., 2019; Nguyen et al., 2022). Sowing and harvesting schedules are based on local weather conditions and information recommended by the local extension agency (Dang et al., 2021). CCSs of winter–spring (WS) and summer–fall (SF) crops begin around the third week of November and third week of March (Table 1).
Information about seeds, fertilizer rate, and irrigation water in the study area
Tabelle 1. Informationen zu Saatgut, Düngermenge und Bewässerungswasser im Untersuchungsgebiet
Crop | Seeds (kg/ha) | Fertilizer rate (kg/ha) | Rain fed (mm) | Required irrigation (mm) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
IS | DS | MS | LS | IS | DS | MS | LS | IS | DS | MS | LS | ||
WS | 80 | 60 | 100 | 50 | 130 | 33.4 | 188.2 | 34.7 | 25.6 | 50 | 18 | 60 | 50 |
Stf | 100 | 70 | 100 | 60 | 140 | 46.4 | 73.7 | 192.3 | 306.5 | 40 | 45 | 10 | 0 |
IS: initial stage; DS: development stage; MS: middle stage; LS: late stage
To shorten the cultivation period as well as optimize economic efficiency, farmers adapt cultivation practices including limiting the number of seeds per hectare and using highquality rice varieties (e.g., OM18, OM4900, OM5451, GKG1, and GKG9) and short rice varieties with a life cycle varying from 85 to 95 days. These varieties have the advantages of resistance to brown planthopper, salt tolerance, and a high yield, ranging from 5 to 9 tons/ha. To reduce production costs, farmers have applied the “
The physical and chemical properties of soil across the study area
Tabelle 2. Die physikalischen und chemischen Eigenschaften des Bodens im gesamten Untersuchungsgebiet
Variables | Soil depth layers (cm) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Ha Tien | Kien Luong | Rach Gia | ||||||||||
Layer | 0–10 | 10–20 | 20–30 | 30–40 | 0–10 | 10–20 | 20–30 | 30–40 | 0–10 | 10–20 | 20–30 | 30–40 |
Sand (%) | 24.5 | 27.6 | 22.7 | 19.4 | 25.7 | 27.9 | 24.8 | 19.7 | 22.7 | 24.1 | 24.8 | 19.8 |
Silt (%) | 60.7 | 55.8 | 51.9 | 48.7 | 55.2 | 51.8 | 53.8 | 51.1 | 51.9 | 48.7 | 55.4 | 52.3 |
Clay (%) | 14.8 | 16.6 | 25.4 | 31.9 | 19.1 | 20.3 | 21.4 | 29.2 | 25.4 | 27.2 | 19.8 | 27.9 |
Soil features | Silty clay loam | Silt loam | Silty clay loam | Silty clay loam | Silt loam | Silt loam | Silt loam | Silty clay Loam | Silt loam | Silt loam | Silt loam | Silty clay loam |
FC (% vol) | 40 | 39 | 42 | 38 | 33 | 33 | 33 | 44 | 33 | 33 | 33 | 44 |
PWP (% vol) | 22 | 18 | 19 | 19 | 13 | 13 | 13 | 23 | 13 | 13 | 13 | 23 |
SAT (% vol) | 52 | 46 | 52 | 52 | 46 | 46 | 46 | 52 | 46 | 46 | 46 | 52 |
BD (g/cm3) | 1.12 | 1.21 | 1.25 | 1.31 | 0.98 | 0.89 | 0.96 | 1.02 | 0.97 | 0.94 | 0.99 | 1.13 |
TAW (mm/m) | 210 | 200 | 210 | 210 | 200 | 200 | 200 | 210 | 200 | 200 | 200 | 210 |
K+ (cmol/kg) | 0.21 | 0.18 | 0.18 | 0.17 | 0.19 | 0.18 | 0.19 | 0.22 | 0.19 | 0.18 | 0.18 | 0.20 |
Na+ (cmol/kg) | 0.13 | 0.12 | 0.11 | 0.12 | 0.10 | 0.12 | 0.13 | 0.14 | 0.11 | 0.12 | 0.12 | 0.14 |
Ca2+ (cmol/kg) | 3.6 | 3.8 | 3.2 | 3.3 | 3.7 | 3.6 | 3.8 | 3.9 | 3.8 | 3.7 | 3.9 | 4.0 |
Mg2+ (cmol/kg) | 0.95 | 0.84 | 0.79 | 0.92 | 0.83 | 0.85 | 0.86 | 0.99 | 0.84 | 0.86 | 0.82 | 0.97 |
pH (H2O) | 5.5 | 5.3 | 5.6 | 5.7 | 5.7 | 5.4 | 5.1 | 5.0 | 5.8 | 5.5 | 5.6 | 5.0 |
FC: field capacity; PWP: permanent wilting point; SAT: saturated hydraulic conductivity; BD: bulk density; TAW: total available soil water
Temperature and rainfall data and other climate variables for the period 2000–2021 at Kien Luong station (not show Ha Tien and Rach Gia stations) were collected from the Southern Regional Hydrometeorological Center, Vietnam (Figure 3). Specifically, model validation was conducted during the period 2000–2010 and model calibration was performed during the period 2011–2021.
AquaCrop is a crop model that was developed by Food and Agriculture Organization to calculate the grain and biomass yields of several crops under different weather conditions (Shrestha et al., 2016; Dang et al., 2021). An advantage of the AquaCrop model is that it is easy to use based on the link between climate, soil, and management modules (Dang et al., 2021). The AquaCrop model requires a small number of input variables for the simulation, and its performance has been validated as both efficient and accurate (Greaves and Wang, 2016; Shrestha et al., 2016).
In the model, actual grain yield (Ya) is defined based on the relationship between maximum grain yield (Ym) and water stress, which is described through the maximum evapotranspiration (ETx) and actual evapotranspiration (ET). Specifically, Ym responses to irrigation water are calculated using Eq. (1):
Finally, grain yield (Y) (Eq. 5) is defined based on multiplying the harvest index (HI) with aboveground biomass production (B) and the stresses (fHI).
To save time and increase the performance of the model, a sensitivity analysis was conducted in model simulations. Determining the sensitivity parameters for the simulation model is a necessary procedure (Dang et al., 2021). In this study, a sensitivity analysis was conducted to define the optimum values for simulating CCSs in Kien Giang Province. The optimal values of the main variables are presented in Table 3.
The main parameters used to simulate the crop cultivation schedules
Tabelle 3. Die wichtigsten Parameter zur Simulation der Anbaupläne für Pflanzen
Base temperature | 5.0 | 4.80 |
Cut-off temperature | 30 | 28.6 |
Canopy cover per seedling at 90% emergence (CCo) | 5.00 | 4.85 |
Soil water depletion threshold for stomata control – upper | 0.50 | 0.48 |
Shape factor for water stress coefficient for canopy senescence | 3.00 | 2.90 |
Decline in crop coefficient after reaching maximum canopy cover | 0.3% | 0.3% |
Crop coefficient for transpiration at CC (100%) | 1.00 | 0.95 |
Leaf growth stress coefficient curve shape | 3.0 | 2.84 |
Normalized water productivity (WP) | 15% | 14.6% |
Allowable maximum increase (%) of specified HI | 10 | 9.4 |
Model validation and calibration were conducted by comparing the simulated results and observed rice grain yield based on the index of agreement (d), the coefficient of determination (
Comparison of observed rice grain yields with the simulated results
Tabelle 4. Vergleich der beobachteten Reiskornerträge mit den simulierten Ergebnissen
Year | Model validation | Year | Model calibration | ||
---|---|---|---|---|---|
Observed | Simulated | Observed | Simulated | ||
2000 | 7.10 | 6.98 | 2011 | 6.64 | 6.68 |
2001 | 7.26 | 7.14 | 2012 | 7.12 | 6.86 |
2002 | 6.42 | 6.31 | 2013 | 7.28 | 7.16 |
2003 | 6.54 | 6.77 | 2014 | 6.57 | 6.83 |
2004 | 6.72 | 6.82 | 2015 | 6.13 | 6.29 |
2005 | 6.35 | 6.57 | 2016 | 6.44 | 6.39 |
2006 | 6.97 | 6.76 | 2017 | 7.25 | 7.34 |
2007 | 7.13 | 6.96 | 2018 | 7.34 | 6.99 |
2008 | 7.35 | 7.27 | 2019 | 6.35 | 6.48 |
2009 | 7.58 | 7.51 | 2020 | 6.78 | 6.85 |
2010 | 6.83 | 6.98 | 2021 | 7.40 | 7.35 |
Validation was based on comparison of the simulated results with observed yields for the WS and SF crops during the period 2000–2010, while calibration was based on comparison of the simulated results with observed yields for the WS and SF crops during the period 2011–2021 (Figure 4).
The validation procedure found high correlations between observed and simulated yields according to d,
The calibration and validation results of rice grain yield corresponding to winter–spring and summer–fall vegetation seasons
Tabelle 5. Die Kalibrierungs- und Validierungsergebnisse des Reiskornertrags entsprechen den Vegetationsperioden Winter-Frühling und Sommer-Herbst
Crop | Validation | Calibration | ||||||
---|---|---|---|---|---|---|---|---|
d | RMSE | MAE | d | RMSE | MAE | |||
WS | 0.83 | 0.83 | 0.17 | 14.9% | 0.86 | 0.85 | 0.11 | 12.6% |
SF | 0.76 | 0.77 | 0.21 | 17.6% | 0.78 | 0.79 | 0.16 | 13.4% |
In recent years, CCRs have been facing challenges caused by climate change and sea level rise. RCPs of Kien Giang Province are facing the challenges of saline intrusion, drought, lack of irrigation water, and sea level rise (Dang et al., 2021). According to Vo et al. (2021), climate change is expected to affect CCRs and, especially, lowland cultivation paddies along the coast of Ken Giang province (Dang et al., 2021). Analysis of CCSs for the WS and SF vegetation seasons based on consideration of input temperature and rainfall pointed out that the rice grain yield of both the WS and SF crops could increase if CCSs were shifted to adapt to changing climate conditions (Figure 5).
For the WS crop, the results showed that bringing CCS forward by around 20 days will increase the rice grain yield by 3.5% to 7.8%, while delaying sowing of the SF vegetation season by 10–20 days will increase the rice grain yield by 2.1% to 5.6% (Table 6). Based on the simulated rice grain yields, a shift in CCSs from baseline would be appropriate when taking into consideration changes in climate variables (e.g., temperature, precipitation, and evapotranspiration).
Rice grain yield response to change in the crop cultivation schedules (sowing date) in the winter–spring and summer–fall vegetation seasons
Tabelle 6. Reaktion des Reiskornertrags auf Änderungen der Anbaupläne (Aussaattermin) in der Winter-Frühlings- und Sommer-Herbst-Vegetationsperioden
Sowing date | Winter–spring | Period | Summer–fall | ||
---|---|---|---|---|---|
Rice grain yield (ton/ha) | Changed trend (%) | Rice grain yield (ton/ha) | Changed trend (%) | ||
7.4 | - | 6.5 | - | ||
30 | 7.3 | −1.6 | 30 | 6.6 | 1.9 |
25 | 7.1 | −3.8 | 25 | 6.7 | 2.7 |
20 | 6.9 | −6.7 | 20 | 6.9 | 5.6 |
15 | 7.0 | −5.4 | 15 | 6.7 | 2.5 |
10 | 7.0 | −4.9 | 10 | 6.6 | 1.3 |
5 | 7.2 | −2.1 | 5 | 6.4 | −0.8 |
−5 | 7.5 | 0.9 | −5 | 6.3 | −2.4 |
−10 | 7.6 | 2.3 | −10 | 6.3 | −3.7 |
−15 | 7.8 | 5.7 | −15 | 6.1 | −5.8 |
−20 | 8.0 | 7.8 | −20 | 6.2 | −3.9 |
−25 | 7.7 | 4.3 | −25 | 6.4 | −1.6 |
−30 | 7.5 | 1.1 | −30 | 6.5 | 0.5 |
According to the local agricultural agency, the “three reductions and three increases” program combined with changing CCS could reduce the amount of seed sown by 30–50 kg/ha, the amount of fertilizers of all kinds by 10–45 kg/ha, and the production costs by around 4,195,000 VND/ha.
This study evaluated the impacts of weather factors on the coastal cultivation regions of Kien Giang Province to determine the optimal crop cultivation schedules for rice paddies. The simulations found that the rice yield of both the winter–spring and summer–fall vegetation seasons could be increased if the crop cultivation schedules were shifted to adapt to changing climate factors. Based on the results, the current crop cultivation schedules are no longer appropriate for current climatic conditions, which have been greatly dominated by climate variability.