Estimation of Crop Water Requirement and Irrigation Scheduling of Rice in Southeastern Region of Bangladesh Using FAO-CROPWAT 8.0

Abstract

Water resource is scarce in most of the areas of Bangladesh during dry period due to changing climate impact. The shortage of water resources is endangering the recently achieved self-sufficiency of food production of Bangladesh. Thus, further research is necessary to increase water use efficiency and preserve the e-flow of rivers during the dry season while sustaining the stability of food production even at low usable water supply. The study was conducted to estimate irrigation requirements and irrigation scheduling for rice crop. The study area involved four districts (Cumilla, Brahmanbaria, Chandpur, and Feni) of the southeastern region of Bangladesh since 46.92% of the total land area is used for rice cultivation in this region. FAO developed CROPWAT 8.0 model was adopted for estimating irrigation water requirement, effective rainfall, and irrigation scheduling during Aus, Aman, and Boro rice. Historical climatic data including temperature, humidity, sunshine hours, wind speed (1981–2010) from three weather stations, and rainfall data from four rainfall stations were used as input to the model. The model calculated reference evapotranspiration (ET_o) using the FAO Penman–Monteith method and USDA soil conservation method was used to find effective rainfall. Results showed that, Boro rice required the maximum irrigation than other seasons due to less seasonal rainfall. T. Aus rice estimated irrigation water in its early growing period, whereas T. Aman rice estimated it in later growing period because of ununiformed rainfall distribution. Boro rice demanded irrigation in its whole growing period in each study location. Practically, farmers applied more irrigation water to their rice field than the model estimated amount. This was the result of lack of farmers’ knowledge about water management technologies. This study found the model suitable for generating irrigation schedule with the necessary split application dates without yield reduction. CROPWAT model has the potentiality to make appropriate irrigation scheduling and save irrigation water. It will save huge energy, cost of production and reduces pressure on water sources.

1 Introduction

With the rapid population growth, cereal food demand will also be increasing and providing freshwater for the world population has also become a major challenge. So water management and irrigation have to be optimized fully to gain maximum yield without water loss. Improvement in water use efficiency will also help in achieving the full benefit of other production inputs, like fertilizers, high-quality seeds, tillage, labor, energy, and machinery [1]. Water management can be done efficiently by CROPWAT [2]-aided irrigation scheduling which ensures the application of the desired amount of water at the right time to conserve water resources. For design and management of irrigation schemes, CROPWAT is a Food and Agricultural Organization (FAO) recommended model, designed by Smith [2] which assesses reference evapotranspiration (ET_o), crop evapotranspiration (ET_c) and irrigation water requirements by integrating climate, crop and soil data [3]. This model was developed by Land and Water Development Division of (FAO). Evaluation of irrigation practices of farmers and estimating the performance of crops under irrigated and non-irrigated conditions can also be done with this program in addition to irrigation scheduling [4]. Presently, the largest amount of water is consumed by agriculture and most of it is lost through evapotranspiration [5]. Rice is a staple food in the tropical region of the world. In Asia, more than 90% of the world’s rice (Oryza sativa L.) is produced and consumed [6]. For a water-loving crop like rice, standing water is essential in the initial stages for supple plant growth and yield maximization. Evapotranspiration loss in this process is very high, and the cost of over irrigation results in higher production prices. Standing water also creates a salinity problem causing the land to become unsuitable for cultivation. Proper scheduling can minimize water logging problems by reducing the drainage requirements and lower fertilization costs by holding surface runoff and deep percolation (leaching) to a minimum.

Constructing dam and reservoirs upstream of transboundary rivers have been causing a shortage of flow in the dry season in Bangladesh, resulting in freshwater scarcity and salinity intrusion in the southern region of Bangladesh. The construction and maintenance of irrigation canal system are not cost-effective. Percolation during the canal flow raises the groundwater level creating marshlands and salinity in the soil along canals. The productivity of the irrigated rice ecosystem is threatened by decreasing water availability since 50% of freshwater is used for rice production in Asia [7] and irrigated rice culture fulfills 75% of the global demand of rice [8]. Water use efficiency may be improved through genetic engineering and biotechnology. But being a complex trait, scientists are skeptical about it [9].

Bangladesh is a riverine country with an average rainfall of 2000 mm. But the temporal and spatial distribution of rainfall is highly uneven and resulting in a dry spell of 5–6 months. Boro season varieties are mainly cultivated during the dry season for its higher yield and better rice quality. Irrigation is mandatory in this season, and water management must be done to minimize the cost. Production of Aus and Aman season can also be increased through irrigation if scheduling is done effectively. Hence, in this paper, an attempt has been made to estimate the irrigation requirement and time of irrigation for rice in the southeastern region of Bangladesh for all three seasons using CROPWAT 8.0.

2 Materials and Methods

2.1 Site Selection

The study area was in the southeastern region of Bangladesh (23.96–23.01°N and 90.66–91.39°E, altitude 5–15 m above mean sea level). Four districts, namely Cumilla, Feni, Chandpur, Brahmanbaria, were selected for the study. The total area of the selected study area is almost 7705.81 sq. km, and the total irrigated area is 3619.253 sq. km [10]. Therefore, almost 46.92% of the total area is irrigated in dry (Boro) season. The study area mainly belongs to agroecological zone (AEZ)-19 (Old Meghna Estuarine Floodplain), AEZ-16 (Middle Meghna River floodplain), AEZ-17(Lower Meghna River floodplain), and AEZ-23 (Chittagong Coastal Plains) [10]. Among these zones, AEZ-19 covers most of the land area [10] and the soil type of study area is mostly silt loam [11].

2.2 Data Collection

The CROPWAT model simulates crop water requirement and scheduling under various types of climatic, crop, and soil conditions. It calculates ET_o by using minimum and maximum temperature (°C), relative humidity (in %), wind speed (km/h), and sunshine hours (hours). The monthly values of these parameters were collected from the Bangladesh Meteorological Department (BMD) from 1981 to 2010 timespan.

From these, the model calculated radiation (MJ m⁻² day⁻¹) and used it to calculate ET_o for every 10 days (defined as “decade” by FAO) and then accumulated it to generate monthly data.

Climatic data were not available for the Brahmanbaria District. So, climatic data of the Cumilla District were also used for calculating radiation and reference evapotranspiration (ET_o) for the Brahmanbaria District.

Rainfall data of Cumilla, Chandpur and Feni districts were collected from Bangladesh Meteorological Department (BMD) and the same from Bangladesh Water Development Board (BWDB) for Brahmanbaria District.

Rice is cultivated in three seasons (Aus, Aman, and Boro) in Bangladesh. For this research work, all three seasons were considered. BRRI dhan48 (110 days duration), BRRI dhan71 (114 days duration), and BRRI dhan89 (158 days duration) were taken for Aus, T. Aman, and Boro varieties, respectively. 10th May, 30th July, and 10th January were taken as the transplanting date for the selected varieties, respectively. Rice growing period was divided as:

• I. Nursery/land preparation (seedling stage 20 days in Aus, 25 days in T. Aman, and 40 days in Boro season).

• II. Initial stage (transplanting to seedling establishment, usually 10 days in all season).

• III. Crop development stage (tillering to panicle initiation, 20 days in Aus, 19 days in T. Aman, and 48 days in Boro season).

• IV. Mid-stage (panicle initiation to 100% flowering, 30 days for all three varieties]).

• V. Late stage (flowering to maturity, 30 days for all three varieties).

Maximum rooting depth and puddling depth were considered 40 cm and 15 cm, respectively. The default values were taken from the model for crop coefficient ($K_{{c_{{{\text{dry}}}} }}$and $K_{{c_{{{\text{wet}}}} }}$), critical depletion fraction, yield response factor (Ky). 1.05 m, 1.08 m, and 1.06 m were taken as the crop height for the varieties, respectively [12].

Hossain [13] reported the moisture content of silt loam soil at field capacity and wilting point were 26.8% and 12.9%, respectively, whereas drainable porosity was approximately 18% [12]. The bulk density of silt loam soil was 1.5 g cm-3 [13]. The maximum infiltration rate of the soil was considered 40 mm day⁻¹.

2.3 Calculation of Reference Evapotranspiration (ETo)

CROPWAT model calculated reference evapotranspiration using the FAO Penman–Monteith method [14]. The Penman–Monteith equation can be written as

$$ {\text{ET}}_{{\text{o}}} = \frac{{0.408{ }\Delta { }\left( {R_{n} - G} \right) + \gamma \frac{900}{{{\text{T}} + 273}}u_{2} \left( {e_{s} - e_{a} } \right)}}{{\Delta + \gamma \left( {1 + 0.34{ }u_{2} } \right)}} $$

(1)

where ET_o = reference evapotranspiration [mm day⁻¹], R_n = net radiation at the crop surface [MJ m⁻² day⁻¹], G = soil heat flux density [MJ m⁻² day⁻¹], T = mean daily air temperature at 2 m height [°C], u₂ = wind speed at 2 m height [m s⁻¹], e_s = saturation vapor pressure [kPa], e_a = actual vapor pressure [kPa], e_s−e_a = saturation vapor pressure deficit [kPa], ∆ = slope vapor pressure curve [kPa °C], γ = psychrometric constant [kPa °C⁻¹], and 900 is a conversion factor.

2.4 Calculation of Effective Rainfall

CROPWAT model considers four methods for effective rainfall calculation. These are fixed percentage, FAO/AGLW formula, empirical formula, and USDA soil conservation service method. In this study, the USDA soil conservation service method [15] was used.

$$ P_{{{\text{eff}}\left( {{\text{dec}}} \right)}} = P_{{{\text{dec}}}} {*}\frac{{\left( {125 - 0.6 * P_{{{\text{dec}}}} } \right)}}{125}{\text{ for }}P_{{{\text{dec}}}} \le \frac{250}{3}{\text{ mm}} $$

(2)

$$ P_{{{\text{eff}}\left( {{\text{dec}}} \right)}} = \frac{125}{3} + \left( {0.1 * P_{{{\text{dec}}}} } \right) {\text{for}}\,P_{{{\text{dec}}}} > \frac{250}{3}{\text{ mm}} $$

(3)

where P_effective = effective monthly rainfall (mm), P_dec = total decade rainfall (mm).

2.5 Estimation of irrigation water requirement and time of application

The amount of water that is supplied to meet the water requirement of the crop is called crop water requirement. It is determined by subtracting efficient rainfall from crop evapotranspiration (ET_c). ET_c denotes the amount of water that is lost through evapotranspiration and is found by multiplying crop coefficient (K_c) with reference evapotranspiration. Since default values were taken for K_c, the model estimated Kc dry value was 0.70, 1.05, and 0.7 in nursery, development, and late stages, respectively. Whereas K_c wet value was 1.20, 1.20, and 1.05 in the nursery, development, and late stages, respectively. The crop water requirement was estimated by using [16].

$${\text{CWR}}_{i} = \mathop \sum \limits_{t = 0}^{T} \left( {k_{{c_{i} }} \cdot {\text{ET}}_{{\text{o}}} - P_{{{\text{eff}}}} } \right)$$

(4)

where Kc_i = crop coefficient of the given crop during the growth stage t and T is the final growth stage. The crop evapotranspiration ET_c = K_c × ET_o where K_c is crop coefficient and ET_o = reference crop evapotranspiration (mm day -1).

For irrigation scheduling, it was considered that irrigation will be done at 0 mm standing water depth and water will be refilled up to 50 mm water depth. Irrigation efficiency was considered 80%. Irrigation scheduling was done for all three seasons (Aus, T. Aman, and Boro) in all four districts in the study area.

3 Results and Discussions

3.1 Reference Evapotranspiration

Reference evapotranspiration (ET_o) for three districts was estimated and shown in Fig. 1. The mean annual ET_o of rice crop was 1398 mm year⁻¹ for four selected districts in the southeastern region of Bangladesh. For Chandpur and Feni, observed ET_o values were higher (more than average value of 118 mm and 114 mm, respectively) from March to June and lower from July to February. For Cumilla District, observed ET_o values were higher (more than average value of 116 mm) from March to September and lower from October to February. In all three districts, the highest (155–168 mm) ET_o was observed in May and the lowest (77–85 mm) in January. In March to May, low relative humidity and high temperatures resulted in increased ET_o. In March to May, low relative humidity and high temperatures resulted in increased ET_o. On the other hand, high humidity along with low temperature and sunshine hours reduced ET_o in the rainy season. Lower ET_o observed in the winter (November–February) was caused by lower values of temperature, sunshine, and wind speed.

Fig. 1

Spatiotemporal distribution of monthly normal evapotranspiration in Cumilla, Chandpur, and Feni districts

3.2 Rainfall vs Crop Evapotranspiration

Rainfall, effective rainfall, evapotranspiration, and crop evapotranspiration requirement are shown in Fig. 2. Rainfall data analysis provides an approximate average of 2100 mm rainfall in Cumilla, Chandpur, Brahmanbaria districts with an average effective rainfall of 1085 mm year⁻¹. For the Feni District, the observed rainfall is approximately 3100 mm with an effective rainfall of 1203 mm year⁻¹. Maximum rainfall was observed in July, and almost 80% of the total rainfall occurs between April and September. From May to October, rainfall is almost enough to meet the evapotranspiration requirement. However, imbalanced spatial and temporal distribution may account for supplemental irrigation in some areas. For the other six months of the year, irrigation is necessary to avoid yield reduction.

Fig. 2

District-wise distribution of average rainfall, effective rainfall, reference evapotranspiration over the months and rice crop evapotranspiration in three seasons (Aus, T. Aman & Boro)

3.3 Rice evapotranspiration and Irrigation requirement

Tables 1, 2, and 3 show the crop water requirements for all three seasons in four districts inside the study area. For all four districts of the study region, the CROPWAT model estimated crop water requirement and irrigation requirement during its growing period. BRRI dhan48, BRRI dhan71, and BRRI dhan89 were selected for Aus, Aman, and Boro season. For T.Aus rice transplanted on 10th May accounted crop evapotranspiration (ET_c) of a range of 478–500 mm. For seedling and initial stages, effective rainfall was enough to fulfill the water requirement. But for development and mid-stages, supplemental irrigation is necessary. In late stages, monsoon season rainfall was enough to satiate the water requirement as shown in Table 1. Transplanting date for Aman is assumed 30th July with a lifespan of 114 days. Almost no irrigation is required for this season and crops are considered rainfed in this season as shown in Table 2. But uneven spatial distribution may require a little irrigation to gain maximum yield. For Boro season, 10th January is taken as the transplanting date. Since it is dry season, heavy irrigation requirement is observed as shown in Table 3. Boro season rice yields the highest amount of rice, and so irrigation requirements must be met. The crop coefficient (K_c) values are shown for Cumilla District only.

Table 1 Normal crop water requirement for T.

Table 2 Normal crop water requirement for T. Aman rice at different locations of southeastern region

Table 3 Normal crop water requirements for Boro rice at different locations of southeastern region

3.4 Irrigation Scheduling

Irrigation scheduling for Cumilla and Chandpur districts for all three seasons is shown in Tables 4 and 5, respectively. In Cumilla District, 7 split applications of irrigation were required during T. Aus season with a gross irrigation of 503 mm. Though T. Aman is considered as rainfed crop, the model showed that it requires 340 mm water to avoid yield reduction. Boro season crop is highly irrigation-dependent, and 969 mm irrigation with 14 split application was necessary to fulfill the need. In Chandpur District, the gross irrigation in T. Aus and T. Aman season is 513 mm and 271 mm with 7 and 4 split applications, respectively. But in Boro season, 16 split applications of total 1109 mm water were required to avoid water stress as shown in Table 5.

Table 4 Irrigation scheduling of rice (Cumilla District)

Table 5 Irrigation scheduling of rice (Chandpur District)

Irrigation scheduling for Brahmanbaria and Feni districts for all three seasons is shown in Tables 6 and 7, respectively. In Brahmanbaria District, irrigation requirement remains more or less consistent with Cumilla and Chandpur districts with 505 mm (7 split applications) in T. Aus season. In Aman season, 268 mm (with 4 split applications) irrigation is required which is more or less consistent with Chandpur district. In Boro season, 980 mm irrigation (with 14 split applications) was necessary which is more or less consistent with Cumilla District. Feni District has higher rainfall than the rest of the study area. So lesser irrigation requirement was observed in the district with gross irrigation of 436 mm (6 split application) in T. Aus, 269 mm (4 split application) in Aman, and 1037 mm (15 split application) in Boro season. Since Boro season is the dry season (December–March) of Bangladesh, irrigation requirement for this season is maximum among all seasons.

Table 6 Irrigation scheduling of rice (Brahmanbaria District)

Table 7 Irrigation scheduling of rice (Feni District)

4 Conclusion

The CROPWAT model estimates evapotranspiration, effective rainfall, and crop water requirement and does irrigation scheduling in the study with precision and assurance. Boro season rice requires maximum 1109 mm supplementary irrigation in Chandpur and overall more irrigation than other seasons. T. Aman requires a minimum of 268 mm irrigation in Brahmanbaria which denotes that without irrigation yield reduction might occur. T. Aus requires 489 mm supplementary irrigation an average. These results can be used for further research in the study area. Proper scheduling of irrigation can alleviate yield stress in crop and increase yield to maximum. Lessening cultivation costs will help to ensure food safety for the rapidly growing population of Bangladesh.