Hybrid Energy Powered Smart Irrigation System for Smallholder Farmers: Installation Site and Crop Selection

Abstract

In the context of food-energy-water nexus and uncertainties in climate change, hybrid energy powered smart irrigation system (HEPSIS) is an emerging solution for optimizing both energy and water to boost crop yield. In Pakistan, most of the farmers especially smallholder farmers are currently relying on conventional irrigation practices which result in high water consumptions, high energy consumptions (by means of pumping), low crop yields, and net profit. Prior to design/development, installation, and testing of the HEPSIS, it is essential to know a suitable site and potential food/cash crops which will be irrigated. In this regard, the study aims to select installation site and potential crops. Site suitability is explored for Sindh province from viewpoints of Indus Basin Irrigation System mapping, groundwater table depth/quality mapping, land use land cover, and soil classifications. Furthermore, crop selection analyses are performed by means of a screening matrix approach based on stars to identify two potential food and cash crops. As per the results, Badin, Ghotki, Khairpur, Sanghar, Shikarpur, Larkana, and Thatta are selected as some suitable sites for the proposed HEPSIS. Additionally, wheat and rice are selected as potential food crops whereas cotton and sugarcane are selected as potential cash crops.

1. Introduction

1.1 Background

The agriculture sector plays an essential role in the economy of Pakistan as it contributes about 22.7% of the gross domestic product (GDP) as well as food security. However, the contribution of the agriculture sector to the country’s GDP has been steadily decreasing from 30.4% to 22.7% since 1980 to 2022 [1] because of climate-induced desertification. On the other hand, water scarcity and food demand are increasing corresponding to an increase in the population of the country [2, 3, 4]. Currently, Pakistan is ranked 12th among the most vulnerable countries where agriculture and water resource sectors are significantly influenced by climate change (CC) and it is projected that underdeveloped countries like Pakistan will experience more severe effects on these sectors in upcoming decades [5, 6, 7]. In these perspectives, climate-smart adaptation strategies are necessary for sustainability in the agriculture sector in terms of food security [8]. Figure 1 shows CC’s impact on the agriculture sector and climate-smart adaptation strategies. Among various subsectors of agriculture, the crop sector is an important subsector that contributes about 4.41% to the GDP of the country and ensures food security for a rapidly growing population [1, 10]. The country’s crop sector is primarily dependent on irrigation i.e., artificial application of water to the soil to meet crop water requirements (CWR) that vary with geography, types of crops, and their growing stages [11, 12, 13]. About 90% of freshwater is used by irrigation practices for meeting the CWR. This excessive amount of freshwater consumption exerts huge pressure on freshwater reserves. Additionally, irrigation practices consume a significant portion of energy by means of pumping which results in the emission of greenhouse gases [14]. Figure 2 shows the volume of water utilized for irrigation via pumping, amount of energy consumed for pumping irrigation water, irrigation energy footprint, and irrigation carbon footprint for major crops in Pakistan. Therefore, a trade-off between energy utilization and supply of irrigation water is essential for sustainable agriculture.

Figure 1.

Climate change impacts on agriculture sector with associated climate-smart adaptation strategies [9].

Figure 2.

Pakistan’s map shows (a) volume of water utilized for irrigation via pumping, (b) amount of energy consumed for pumping irrigation water, (c) irrigation energy footprint, and (d) irrigation carbon footprint for major crops, reproduced here from [14].

Indus Basin Irrigation System (IBIS) is the largest component and source of irrigation. However, irrigation system in Pakistan is facing major challenges like depleting water reserves, canal water losses, inappropriate water management practices, and lack of access to modern techniques, thereby reducing crop yields [7, 15, 16]. Different kinds of irrigation systems like sprinklers and drips are being employed worldwide. However, farmers across a major portion of the country employed warabandi system (using canal water) to apply irrigation without knowing the actual CWR. Furthermore, conventional irrigation systems are constrained by salinity, water logging, and low application efficiency [17, 18]. In this regard, renewable energy-operated smart irrigation systems can address the aforesaid dilemma [19]. The smart irrigation system can apply water based on the actual CWR corresponds to growth stages, thereby optimizing water utilization and enhancing crop yield.

1.2 Hybrid energy powered smart irrigation system

Hybrid energy powered smart irrigation system (HEPSIS) involves integration of renewable energy sources (solar/wind), sensor technology, artificial intelligence (AI), machine learning (ML), and internet of thing (IoT) based decision support systems (DSS) as well as mobile app (act as user interface) for controlling and monitoring mechanisms remotely in order to optimize energy and water use in irrigation [20, 21, 22]. Figure 3 shows a working scheme of the HEPSIS. The solar/winds energy ensures a consistent and environment-friendly power supply which can be utilized to operate controllers and pumps for the distribution of irrigation water. The sensors help in collecting real-time data of climatic parameters including temperature, relative humidity, precipitation, wind speed, sunshine hours, solar intensity, and soil moisture levels which depend on the CWR [23]. The AI, ML, and IoT-based DSS receives collected data from the sensors and performs data processing to make decision about irrigation scheduling (when and how much to irrigate) [22, 24]. Figure 4 provides a conceptual scheme of IoT in the smart irrigation system. In the end, the mobile app provides user-friendly interface to farmers remotely for monitoring and controlling irrigation practices and to make decisions accordingly.

Figure 3.

Working scheme of HEPSIS, reproduced here from [9].

Figure 4.

Conceptual scheme of internet of things (IoT) for the smart irrigation system [25].

The remote accessibility saves time and resources and overcomes the need for manual labour by increasing operational efficiency. Furthermore, integration of the HEPSIS with weather forecasting systems helps in boosting the effectiveness. By incorporating real-time climatic and soil moisture data, the HEPSIS can adapt irrigation scheduling based on predicted temperature, and precipitation changes, thereby optimizing water utilization and reducing dependency on manual adjustments [26, 27]. Some benefits of the smart irrigation system for rice crops over conventional systems are presented in Figure 5. By minimizing water usage, farmers can reduce their overall operational costs, improve energy efficiency, and mitigate environmental impacts, such as water pollution and soil erosion.

Figure 5.

Comparison between smart irrigation systems and conventional irrigation systems [28].

In these perspectives, the authors are planning to design, develop, and test HEPSIS for smallholder farmers in Sindh province under the project number: 10039507 funded by Innovate UK. For execution of the project, it is essential to explore suitable sites for installing and testing the system. Additionally, potential food and cash crops need to be explored that will be irrigated with the proposed smart irrigation system. Therefore, the study aims to explore suitable installation sites and crop selection analyses. The site suitability analysis is explored from viewpoints of IBIS mapping, groundwater table/depth, land use land cover (LULC) classification, and soil classification which are available in the literature [29, 30, 31, 32]. For crops selection, seven significant factors for each studied food and cash crop are considered which are listed as (i) cultivated area (thousand acres), (ii) irrigation requirements (mm/season), (iii) water cost (Rs/acre), (iv) net production (thousand tonnes), (v) average yield (kg/acre), (vi) production cost (Rs/acre), and (vii) net profit (Rs/acre). In order to select two potential food and cash crops screening matrix i.e., qualitative methodology based on stars is employed.

2. Materials and methods

Figure 6 shows a flow chart presenting a summary of the research methodology executed in this study. The methodology comprises of two processes namely: site suitability and crops selection. The extensive details of each process are given in the following subsections.

Figure 6.

A flow chart representing the summary of research methodology executed in this study (a) site suitability, and (b) crops selection.

2.1 Study area

In this study, Sindh province is selected as a study area, ranked second in agricultural production in Pakistan. Among a total area of 140, 914 km², about 51.9% of the province is characterized as a rural area and 48.1% as an urban area.

A total number of districts is thirty as shown in Figure 7. According to Köppen climate classification, Sindh experiences a subtropical desert climate, thereby evaporation is higher as compared to other provinces in Pakistan. Mean annual rainfall varied between 100 to 200 mm specifically in the Lower Indus Plain [29]. It is located at an elevation of 24.59 m (80.68 ft) above sea level and in the southern part of the IBIS. The IBIS network enters Sindh at Guddu barrage and four canals namely: Begari, Desert pat feeder, Ghotki, and Rainee offtake. The Guddu barrage meets the agricultural water or irrigation demands of Jacobabad, Kashmore, Qamber Shahdadkot, Shikarpur, and Sukkur districts of Sindh and the Naseerabad district of Balochistan through its four canals.

Figure 7.

Location and districts of the study area [33].

2.2 Site suitability analysis

Referring to Figure 6a, site suitability analysis involves four steps namely: (1) IBIS mapping, (2) groundwater depth and quality, (3) land use land cover classification (LULC), and (4) soil classification. Each step for Sindh province was performed in literature. Therefore, the present study utilizes data of the IBIS mapping [29], groundwater depth [30], groundwater quality [30], LULC classification [31], and soil classification [32] from the cited literature for exploring suitable installation sites.

2.3 Crops selection

Referring to Figure 6b, crops selection involves two steps namely: (1) data collection, and (2) screening matrix. Eight kinds of food and cash crops are selected in order to identify two potential food and cash crops. The food crops include wheat, rice, maize, moong, jowar, gram, onion, and tomato while the cash crops include cotton, sugarcane, rapeseed, chilies, sesame, potato, mango, and potato. Data of seven significant factors that are listed as (i) cultivated area (thousand acres), (ii) irrigation requirements (mm/season), (iii) water cost (Rs/acre), (iv) net production (thousand tonnes), (v) average yield (kg/acre), (vi) production cost (Rs/acre), and (vii) net profit (Rs/acre) are collected for each studied food and cash crop at national and provincial level specifically for Sindh. The data of each significant parameter excluding irrigation requirements for the studied crops is collected from the agricultural marketing information service [34]. The irrigation requirements data for each food and cash crop is obtained from food and agriculture organization (FAO).

Screening matrix methodology is a systematic approach used to estimate and compare various options based on specific criteria. The screening matrix involves assigning star ratings to each parameter in order to compute its suitability in accordance with the specified criteria. The star ratings serve to evaluate how well each option aligns with the defined criteria. A higher star rating indicates a stronger performance or better alignment with the desired criteria. In the context of selecting two potential food and cash crops, stars are assigned to each selected crop based on the normalized score obtained by the relative weightage of the significant factors. The weightage to each significant factor of selected crops is provided using a normalized scoring method as given by Eq. (1). The star ratings corresponding to normalized scores are provided in Figure 8.

Figure 8.

Normalized score range and corresponding stars rating.

$$\mathit{\text{Normalizedscore}}=\frac{\left(\mathit{\text{OriginalValue}}-\mathit{\text{Minimumvalue}}\right)}{\left(\mathit{\text{Maxiumvalue}}-\mathit{\text{Minimumvalue}}\right)}$$E1

3. Results and discussion

3.1 Site suitability analysis

3.1.1 IBIS

IBIS is considered as largest irrigation system throughout the globe, covering a total area of 17.2 million hectares with a length of 2900 km [35]. According to the World Bank Report, the Indus River basin is inhabited by over 300 million people across Afghanistan, China, India, and Pakistan. About half of the basin lies in Pakistan which covers approximately two-thirds of the land and 87% of the country’s population [36]. The IBIS plays a significant role in meeting country’s irrigation requirements and up to 96% of country’s renewable water resources [36]. The river flows through the Himalayas at 18,000 ft to the Sindh plains and ultimately falls into the Arabian Sea. The basin experiences a mean annual flow of 176 billion m³. About 90% of the flow is utilized for meeting irrigation requirements via canals passing through three barrages namely: Sukkur, Guddu, and Kotri as shown in Figure 9. Additionally, the IBIS includes 3 major reservoirs, 12 inter-river link canals, 44 irrigation canals i.e., canal commands (14 in Sindh), 2 headworks, 2 siphons, and 16 barrages. Mostly, groundwater is utilized for the irrigation instead of canal water in regions with a lack of access to the canal water and the amount of surface water availability is insufficient [35].

Figure 9.

IBIS with main cities, reservoirs, link canals, and barrages [29].

3.1.2 Groundwater depth and quality

Groundwater depth and quality analyses for Sindh province were performed by the Pakistan Council of Research in Water Resources (PCRWR) [30]. The results are presented in Figure 10. According to PCRWR data, groundwater table depth lies between 0.2 m to 16.0 m. A major portion of Sindh’s area specifically along Indus River has groundwater depth ranging from 1.6 to 3.0 m. Shallow groundwater table depth i.e., <5 m requires relatively less pumping cost and is most suitable for crop growth. It is observable that Badin, Ghotki, Khairpur, Sanghar, Shikarpur, Larkana, and Thatta lie in the range of the shallow groundwater table depth. On the other hand, most of Sindh’s area has electrical conductivity (EC) greater than 4.0 dS/m. However, water having less EC is most suitable for crop growth. It is observable that the EC of water is relatively less at Badin, Ghotki, Khairpur, Sanghar, Shikarpur, Larkana, and Thatta as compared to other cities.

Figure 10.

Spatial variations of (a) groundwater table depth, and (b) groundwater quality for Sindh, reproduced here from [30].

3.1.3 Land use land cover classification

Land use land cover (LULC) classification is a significant parameter that offers comprehensive information regarding the utilization of landscape. The LULC classification helps in environmental monitoring allowing to identify the areas vulnerable to degradation and support in resource managements. In general, the LULC classification provides a baseline for estimating past and future changes [37]. The LULC classification was performed by Bashir et al., [31] using unsupervised classification in ArcGIS software that plays a significant role in creating LULC classifications, providing valuable information for agriculture, urban planning, environmental management, natural resource assessment, and site suitability analysis [31]. The LULC classification map is presented in Figure 11. It can be observed that Badin, Ghotki, Khairpur, Sanghar, Shikarpur, Larkana, and Thatta mostly lie in cropped areas/agricultural land.

Figure 11.

Land use land cover classification of Sindh, reproduced from [31].

3.1.4 Soil classification

Soil classification provides critical insights for appropriate cultivation practices and crops selection to improve overall yield. Ulain et al., [32] performed the soil classification for Pakistan and results are presented in Figure 12 [32]. It is observable that, the cultivated/agricultural land of Sindh mostly experiences sandy loam, clay loam, and sandy clay loam soil. Both sandy and clay loam soils are preferable for the cultivation of food and cash crops. As per the results of the soil classification, Badin, Khairpur, Sanghar, Shikarpur, Larkana, Thatta Noshahro Feroz, Dadu, and Larkana are the most promising areas for cultivation.

Figure 12.

Soil texture classification map for Pakistan, reproduced here from [32].

3.2 Crops selection

3.2.1 Food crops

Wheat, rice, maize, moong, jowar, gram, onion, and tomato are commonly cultivated food crops in Pakistan. Figure 13 shows temporal variations of cultivated area for studied food crops in Pakistan and Sindh for the period of 1991–2022. The colour gradient shows temporal variations in the cultivated area for each studied food crop. In the last 32 years, the cultivated area for wheat and rice significantly changed with respect to time for Pakistan and Sindh as compared to other studied food crops. The maximum cultivated area of about 22,182 thousand acres was recorded by wheat for Pakistan and 2,920 thousand acres for Sindh during the period of 2021–2022. It is observable that wheat and rice are extensively growing crops in the country. Other food crops in Pakistan and Sindh show relatively less cultivated area trends e.g., moong, jowar, onion, and tomato.

Figure 13.

Temporal variations of cultivated area for studied food crops in Pakistan and Sindh for the period of 1991–2022, data is obtained from [38].

Figure 14 shows temporal variations of net production for studied food crops in Pakistan and Sindh for the period of 1991–2022. It can be noticed that the net production trends for wheat, and rice varied significantly in Pakistan and Sindh with respect to time. During 2021–22, maximum production by wheat was recorded of 26,393.65 thousand tonnes and 3,759.75 thousand tonnes for Pakistan and Sindh, respectively.

Figure 14.

Temporal variations of net production for studied food crops in Pakistan and Sindh for the period of 1991–2022, data is obtained from [38].

The maximum net production of 9,322.67 thousand tonnes was recorded by rice for Pakistan and 2,861.38 thousand tonnes for Sindh. As per Pakistan's economic survey, both wheat and rice crops contributed 1.8% and 0.5% to overall GDP during 2021–22 [1].

Figure 15 shows temporal variations of the average yield for studied food crops in Pakistan and Sindh for the period of 1991–2022. In the case of food crops (wheat and rice), the maximum average yield was recorded of 1,190 kg/acre and 1,287.6 kg/acre for Pakistan and Sindh, respectively for the period of 2021–22. Similarly, the maximum average yield of 1,066.4 kg/acre and 1,530.4 kg/acre was recorded by rice crop for Pakistan and Sindh, respectively. On the other hand, among studied food crops maximum average yield was observed at 5,954 kg/acre and 5,285.2 by onion for both Pakistan and Sindh, respectively. Despite the high cultivated area of wheat and rice, the average yield is less as compared to developed countries because of inappropriate irrigation practices, utilization of farm machinery, and other associated operations.

Figure 15.

Temporal variations of average yield for studied food crops in Pakistan and Sindh for the period of 1991–2022, data is obtained from [38].

Data of significant parameters for each studied food crops for the period of 2021–2022 is utilized for assigning normalized scores. The normalized scores are utilized for developing screening matrix at both country and provincial levels. Table 1 displayed cultivated area, net production, and average yield for each studied food crop used for developing screening matrix in case of Pakistan and Sindh for the period of 2021–2022. Table 2 shows case data of irrigation requirements, water cost, production cost, and net profit of each studied food crop used for developing a screening matrix in case of Pakistan and Sindh. It is worth mentioning that the data of irrigation requirements, water cost, production cost, and net profit of each studied food crops are used for Punjab province as per availability. Therefore, the data is used as a reference for developing a screening matrix for both Pakistan and Sindh province.

Food crop	Cultivated area (Thousand acres)	Net production (Thousand tonnes)	Average yield (kg/acre)
Pakistan
Wheat	22,181.77	26,393.65	1,190
Rice	8,741.19	9,322.67	1,066.4
Maize	4,083.50	10,634.83	2,604.4
Moong	745.66	263.78	353.6
Jowar	189.11	63.94	338
Gram	2,142.46	319.32	149.2
Onion	348.42	2,074.47	5,954
Tomato	124.22	541.28	4,357.2
Sindh
Wheat	2,920.13	3,759.75	1,287.6
Rice	1,869.6	2,861.38	1,530.4
Maize	10.51	4.37	416
Moong	32.35	5.49	169.6
Jowar	25.81	9.91	384
Gram	59.00	22.88	388
Onion	156.41	826.69	5,285.2
Tomato	59.58	176.90	2,969.2

Table 1.

Cultivated area, net production, and average yield for each studied food crop used for developing screening matrix in case of Pakistan and Sindh for the period of 2021–2022 [38].

Food crop	Irrigation requirements (mm/season)	Water cost (Rs/acre)	Production cost (Rs/acre)	Net profit (Rs/acre)
Wheat	325–450	3,109	27,432	38,970
Rice	450–700	14,995	46,773	40,122
Maize	500–800	6,537	49,672	38,187
Moong	300–400	1,963	19,158	26,509
Jowar	450–600	2,500	30,035	15,062
Gram	300–450	299	8,163	6,575
Onion	350–550	7,351	38,734	37,950
Tomato	400–800	4,915	74,699	61,601

Table 2.

Irrigation requirements, water cost, production cost, and net profit of each studied food crop used for developing screening matrix in case of Pakistan and Sindh for the period of 2021–2022 [38].

Normalized scores are computed based on each parameter and stars are assigned corresponding to these scores. Figure 16 shows normalized score cards with cumulative stars obtained by each studied food crop for Pakistan and Sindh. Maximum stars obtained by the studied food and cash crops are selected as potential food crops. Among studied food crops, wheat and tomato crops secured maximum stars in case of Pakistan whereas wheat and rice crops secured maximum stars in case of Sindh. Therefore, wheat and rice crops are selected as potential food crops for Sindh province.

Figure 16.

Normalized score cards from viewpoints of significant factors of each studied food crop with cumulative stars for Pakistan and Sindh.

3.2.2 Cash crops

Cotton, sugarcane, rapeseed, chilies, sesame, potato, mango, and dates are commonly cultivated cash crops in Pakistan. Figure 17 shows temporal variations of cultivated area for studied cash crops in Pakistan and Sindh for the period of 1991–2022. In the last 32 years, the cultivated area for cotton and sugarcane crops significantly changed with time for Pakistan and Sindh as compared to other studied cash crops.

Figure 17.

Temporal variations of cultivated area for studied cash crops in Pakistan and Sindh for the period of 1991–2022, data is obtained from [38].

Among studied cash crops, a maximum cultivated area of about 4,786.44 thousand acres and 1,467.56 thousand acres was recorded by cotton crops for Pakistan and Sindh, respectively. Similarly, a maximum cultivated area of 3,114.31 thousand acres and 729.58 thousand acres was recorded by sugarcane crops in both Pakistan and Sindh. It is observable that cotton and sugarcane crops are extensively growing cash crops in Pakistan.

Figure 18 shows temporal variations of net production for studied cash crops in Pakistan and Sindh for the period of 1991–2022. It can be noticed that the net production trends for cotton, and sugarcane crops varied significantly in Pakistan and Sindh with respect to time.

Figure 18.

Temporal variations of net production for studied cash crops in Pakistan and Sindh for the period of 1991–2022, data is obtained from [38].

During 2021–22, maximum net production of 8,328.81 thousand tonnes and 2,998.41 thousand tonnes by cotton for Pakistan and Sindh, respectively. Likewise, the maximum net production of 88,650.59 thousand tonnes, and 19,460.72 thousand tonnes was observed by sugarcane crops for Pakistan and Sindh, respectively. During 2021–2022, both cotton and sugarcane crops contributed 0.6% and 0.8% to overall GDP of the country as reported by Pakistan Economic Survey [1].

Figures 15,16 and 19 shows temporal variations of the average yield for studied cash crops in Pakistan and Sindh for the period of 1991–2022. In case of cash crops (cotton and sugarcane), the maximum average yield was recorded of 1,740 kg/acre and 1241.2 kg/acre by cotton crop for Pakistan and Sindh, respectively. The maximum average yield was recorded of 28,465.6 kg/acre and 26,673.6 kg/acre by sugarcane crops for both Pakistan and Sindh, respectively. Despite the high cultivated area of cotton and sugarcane in Pakistan and Sindh, the average yield is relatively less as compared to other developed countries because of inappropriate irrigation practices, utilization of farm machinery and other associated operations.

Figure 19.

Temporal variations of average yield for studied cash crops in Pakistan and Sindh for the period of 1991–2022, data is obtained from [38].

Table 3 displays cultivated area, net production, and average yield for each studied cash crop used for developing screening matrix in case of Pakistan and Sindh for the period of 2021–2022. Table 4 shows case data of irrigation requirements, water cost, production cost, and net profit of each studied cash crop used for developing screening matrix in case of Pakistan and Sindh. Figure 20 shows normalized score cards with cumulative stars obtained by each studied cash crop. Among studied cash crops, cotton and sugarcane crops secured maximum stars at both the country and provincial level. Therefore, cotton and sugarcane crops are selected as potential cash crops for Sindh province.

Cash crop	Cultivated area (Thousand acres)	Net production (Thousand tonnes)	Average yield (kg/acre)
Pakistan
Cotton	4,786.44	8,328.81	1,740
Sugarcane	3,114.31	88,650.59	28,465.6
Rapeseed	812.91	454.52	559.2
Chilies	143.6	144.0	1,004
Sesame	493.95	127.98	259.2
Potato	775.32	7,931.07	10,229.6
Mango	393.52	1,844.71	4,687.6
Dates	238.50	838.25	3,514.8
Sindh
Cotton	1,467.56	2,998.41	2,043.2
Sugarcane	729.58	19,460.72	26,673.6
Rapeseed	134.27	52.66	392.4
Chilies	109.6	111.4	1016
Sesame	34.42	6.31	183.6
Potato	1.50	6.00	3,987.6
Mango	146.19	387.40	2,650
Dates	101.38	224.03	2,210

Table 3.

Cultivated area, net production, and average yield for each studied cash crop used for developing screening matrix in case of Pakistan and Sindh for the period of 2021–2022 [38].

Cash crop	Irrigation requirements (mm/season)	Water cost (Rs/acre)	Production cost (Rs/acre)	Net profit (Rs/acre)
Cotton	1,055–1,655	6,349	46,830	32,647
Sugarcane	2,375–3,108	17,751	77,044	70,929
Rapeseed	400–600	1,571	22,535	42,596
Chilies	400–600	8,888	73,061	63,888
Sesame	350–450	1,992	40,335	2,490
Potato	500–700	6,819	99,846	168,492
Mango	500–800	9,000	203,762	87,488
Dates	1,055–1,455	5,000	46,830	32,647

Table 4.

Irrigation requirements, water cost, production cost, and net profit of each studied cash crop used for developing screening matrix in case of Pakistan and Sindh for the period of 2021–2022 [38].

Figure 20.

Normalized score cards from viewpoints of significant factors of each studied cash crop with cumulative stars for Pakistan and Sindh.

4. Conclusion

IBIS is the primary source for meeting irrigation requirements of Pakistan specifically for Sindh. However, irrigation practices in Pakistan are constrained by poor irrigation scheduling or inappropriate estimation of actual crop water requirements corresponds to crop’s growth and development stages resulting in high water consumption and lower crop yields. Furthermore, extensive pumping for irrigation consumes a significant amount of primary energy as well as fresh water. In this regard, hybrid energy powered smart irrigation system (HEPSIS) is an emerging solution for increasing crop yield by optimizing both energy and water. However, site suitability and crops selection analyses are essential before design/development, installation, and testing of the smart irrigation system. Therefore, the study aims to explore suitable installation site and crops for Sindh province. Site suitability analyses involves IBIS mapping, groundwater table depth/quality mapping, land use land cover, and soil classifications. For crops selection, eight kinds of food and cash crops are investigated by using a qualitative methodology based on stars i.e., screening matrix approach by considering potential parameters including cultivated area, irrigation requirements water cost, net production, average yield, production cost, and net profit. Normalized scoring method is utilized for assigning stars to each studied food and cash crop based on their potential parameters. According to results, Badin, Ghotki, Khairpur, Sanghar, Shikarpur, Larkana, and Thatta are selected as some suitable sites for the HEPSIS. Additionally, among studied crops, wheat and rice are selected as potential food crops while cotton and sugarcane are selected as potential cash crops which will be irrigated with smart irrigation system.

Acknowledgments

The authors would like to express their gratitude to Innovate UK’s Energy Catalyst programme (Ayrton Funding provided by the Foreign, Commonwealth Development Office through their Transforming Energy Access Programme) and UK aid for the support and funding provided for the research project titled ‘Hybrid Energy Powered Smart Irrigation System for Smallholder Farmers’ (Project number: 10039507). This financial support from Innovate UK’s Energy Catalyst programme has played a pivotal role in facilitating the successful execution of our research as well as for the completion of this book.

Conflict of interest

The authors declare no conflict of interest.