1 Introduction

Agricultural crop production in a region is primarily driven by (i) natural drivers, i.e. climate (temperature, rainfall, etc.), soil characteristics (texture, pH, etc.), availability of water (surface and ground water) and topographical features and (ii) human led crop management practices (improved varieties, agronomic and plant protection management practices). In this context Punjab state of India though constituting only 1.57% of country’s geographical area (Grover et al. 2017), was the forerunner of Green Revolution in the country. The state is amongst the leading producer state for high quality wheat, paddy, basmati rice, cotton, etc. Traditionally, farmers in Punjab have followed diversified cropping patterns such as maize-wheat, sugarcane-maize-wheat, cotton-wheat, basmati rice-wheat/oilseed brassica. But recently during the last two decades, they have shifted primarily to rice-wheat cropping pattern (Kalia and Dhindsa 2021) and the state has a very high cropping intensity of 190% (Anononymous 2021). This high cropping intensity has altered the climate (Prabhjyot-Kaur et al. 2020), soil (Sharma et al. 2016; Singh and Benbi 2016) and groundwater availability (Sidhu et al. 2020) in the state. So, the sustainability of agricultural entities is at cross roads and the farmers are not able to decide which crops are best suited for sustainable as well as profitable cultivation in their regions.

Agro-Eco-Resource zonation (AERZ) is done by delineating the homogeneous areas for optimization of the available natural resources in a region. This delineation is based on meteorological, groundwater and soil variables with crop suitability criterion as a major consideration (Rajkumar et al. 2020). It helps in identifying the best crops that can be grown in a particular region, which in turn helps in planning and managing agricultural production (Patel and Singh 2015; Yaghoubzadeh et al. 2020). Moreover, AERZ provides the scientific basis for agricultural developmental planning because survival and failure of particular land use or farming system in a given region heavily relies on careful assessment of agro-climatic resources (Widiatmaka 2016). Productivity of a crop in a given area depends primarily on climate, soil, availability of water and topographical features. So, these factors need to be considered before deciding which crop is best suitable for cultivation in that area. Hence based on such factors, grouping of distinguishing regions with different nature helps in making utmost yield of crops that are more suited to the conditions and these ultimately reap maximum monetary profit.

Crop growth and production is directly influenced by crop management factors in congruence with natural resources, i.e. weather, water and soil. So, the targeted area may be divided into several regions based on the key feature of performing developmental actions appropriately in each of the zones (Showkat et al. 2014; Uc-Castillo et al. 2022). The main purpose of this Agro-Eco-Resource zoning is to solve the problem of crop suitability in an area. This also helps the growers decide which crop is most suitable to sow in their respective areas (Jan and Michal, 2016; Saaty 1980).

During 1980’s the agro-climatic zoning in Punjab state was done manually by integrating the agro-climatic and other natural resource data (Mavi 1984). As a result, large amount of agro-ecological data could not be handled easily. This led to the loss of information on spatial variability. In today’s era with the availability of new research tools, particularly with the vital inputs from space technologies such as Geographic Information Systems (GIS) and Analytical Hierarchy Process (AHP), this type of zoning can be done very successfully in a region (Mukherjee and Singh 2020; Arunbose et al. 2021; Topuz and Deniz 2023). The Analytic Hierarchy Process (AHP) is a decision-making technique (Saaty 2008) that is widely used in various fields, including agriculture (Tashayo et al. 2020; Yaghoubzadeh et al. 2020), to determine the relative importance of different factors involved in decision-making. It also allows us to store, analyse and visualize geographical data such as land use (Showkat et al. 2014), soil types (Sharma et al. 2016), topography (Bisht et al. 2013), climate (Geletič & Lehnert 2016), and other relevant information. The application of AERZ using GIS and AHP has been demonstrated in various studies, including the determination of suitable rural settlement areas (Fischer et al. 2000), crop suitability analysis for sustainable land-use planning (Castorena et al. 2023), and the demarcation of landslide susceptible zones (Thomas et al. 2021). The Global Agro-Ecological Zoning (GAEZ) framework further exemplifies the integration of GIS and AHP in assessing the potential productivity and suitability of land for different crops, taking into account agro-climatic constraints and soil and terrain conditions (Ahmed & Sheikh Suleimany, 2023). In Indian context, Sunita et al. (2023) conducted a study on agricultural land suitability for pearl millet in Rajasthan utilized AHP to weigh different criteria, including rainfall and soil properties, showcasing how AERZ can optimize crop production. Similarly, in the Vizianagaram district, the combination of remote sensing (RS) and GIS has been utilized for crop-land suitability analysis, focusing on parameters like slope, elevation, rainfall, and soil texture. This approach helps in identifying areas with varying degrees of suitability for agriculture, thereby guiding optimal land use (Chatterjee et al. 2023).

In the present study, a combination of AHP and GIS techniques were used for classifying the entire Punjab state into different regions on the basis of 8 major factors i.e. minimum temperature, maximum temperature, rainfall, soil organic carbon, soil texture, soil pH, groundwater depth and groundwater level fluctuations. Mean values of the data were taken into consideration and incorporated in ArcGIS as separate data maps (Wart et al. 2013).The final maps of the Agro-Eco-Resource zonation were then validated using the productivity data of the major crops, i.e. rice, wheat and later rice-wheat cropping system practised in state.

2 Materials and methods

2.1 Description of the study area

Punjab state is situated in the northwest region of India with a latitudinal extent of 29°30′ N–32°32′ N and longitudinal extent of 73° 55′–76° 50′E, at an average elevation of 300 m above sea level and between the great river systems of the Indus and the Ganges. It encompasses an area of 50,362 sq. km i.e. 1.5% of India’s geographical area but contributes 21% rice and 31% wheat to food grain pool of the country (Anononymous 2023). The state is edged by plains of Pakistan on the west, humid hilly terrains of Jammu-Kashmir on the north and Himachal Pradesh on northeast and by semi-arid plains of Haryana and arid sandy terrains of Rajasthan on the south. On the basis of topography, climate and soil characteristics the state has been subdivided into five agro-climatic zones, i.e. sub-mountain undulating zone in north east, undulating plain zone, central plain zone, western plain zone and western zone (Mavi 1984; Grover et al. 2017). A sixth zone referred to as “flood plain zone” is considered signifying the fertile soils nurtured by the rivers flowing through the state. The study area is depicted in Fig. 1.

Fig. 1
figure 1

Study area map of Punjab state

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2.2 Data used for zonation

The data on meteorological, groundwater and soil variables were collected and then considered for further analysis (Patel and Singh 2015) and are listed below:

2.2.1 Minimum temperature

Minimum temperatures of an area or place is the lowest temperature at that place in a given time period. The lowest possible temperature is -273 °C, or 0 K. Minimum air temperatures affect night time plant respiration rates and can potentially reduce biomass accumulation and crop yield (Malhi et al. 2021). Average minimum temperature in the research region was found to be in range of 16.0–18.0oC. The minimum temperature data was collected across Punjab for last decade (2008–2018) for the analysis from gridded data of the India Meteorological Department. The layout for the minimum temperature was generated using ArcGIS map software by incorporating the data in it and its thematic map is shown in Fig. 2.

Fig. 2
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Spatial variation of Minimum Temperature (oC) during 2008–2018

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2.2.2 Maximum temperature

Maximum temperature is the highest temperature at a place in a given time period.

There is no limit for maximum temperature. It can go very high, upto an extent where it cannot be measured. High temperature limits the yield and affects various growth stages in plants. The reproductive stage is the most vulnerable phase of the crop’s entire lifespan and this vulnerability during the reproductive stage leads to significant depletion in seed set and crop yield (Malhi et al. 2021). Average maximum temperature in the research region was found to be in range of 30.0–32.0oC. The maximum temperature data was collected across Punjab for last decade (2008–2018) for the analysis from gridded data of the India Meteorological Department. The thematic layout for the maximum temperature was generated using ArcGIS map software by incorporating the data in it and is shown in Fig. 3.

Fig. 3
figure 3

Spatial variation of Maximum Temperature (oC) during 2008–2018

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2.2.3 Rainfall

Rainfall is the most frequent type of precipitation in the research region and average rainfall range of the region was found to be between 200 and 1400 mm. Rainfall data has been collected across Punjab over the decade 2008–2018 for the analysis from gridded data of the India Meteorological Department. The layout for the precipitation was generated using ArcGIS map software by incorporating the rainfall data in it and its thematic map is shown in Fig. 4.

Fig. 4
figure 4

Spatial variation of Rainfall (mm) during 2008–2018

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2.3 Soil data

2.3.1 Soil organic carbon

Soil organic carbon (SOC) is a major contributor to overall soil health, agriculture, climate change, and food solutions. It is natural energy storage, derived from soil organic matter and considered a highly valued earth’s biopolymer. SOC improves soil biological, chemical, and physical properties including water-holding capacity, and structural stability (Kay 2018). The SOC was observed to be in medium range (0.40–0.75%) amongst different sub regions of the state. The soil organic carbon map of Punjab has been acquired from the Department of Soil Science, Punjab Agricultural University and its thematic map is presented in Fig. 5.

Fig. 5
figure 5

Spatial variation in soil Organic Carbon (%) in the state

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2.3.2 Soil texture

Soil texture is the average size of the soil particle which depends on the relative proportion of sand, silt and clay in the soil. Soil texture is interrelated with the soil fertility and quality in the long term. The soil texture is associated with soil porosity, which in turn regulates the water holding capacity, gaseous diffusion and water movement that determines the soil health (Upadhyay and Raghubanshi 2020). Two major soil textures i.e. sandy clay loam and sandy loam were observed, apart from some patches of loamy sand and loam. The soil texture map has been acquired from the Department of Soil Science, Punjab Agricultural University and its thematic map are shown in Fig. 6.

Fig. 6
figure 6

Spatial variation in soil texture in the state

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2.3.3 Soil pH

The soil pH influences both the availability of soil nutrients to plants and also how the nutrients react with each other i.e. at a low pH, many elements become less available to plants, while others such as iron, aluminium and manganese become toxic to plants (Neina 2019).

The soil pH data across different locations of Punjab state has been collected by the Department of Soil Science Punjab Agricultural University and mapped using spatial analyst (inverse weighted distance) tool of ArcGIS. The pH was found to vary in range of 7.0 to 9.0. The soil pH data was integrated into ArcGIS software and the thematic layout for the soil pH is presented in Fig. 7.

Fig. 7
figure 7

Spatial variation in soil pH in the state

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2.4 Groundwater data

2.4.1 Groundwater depth

Groundwater is water that exists underground in saturated zones beneath the land surface and the depth at which soil pore spaces or fractures and voids in rock become completely saturated with water is known as “water table”. Ground water level is an important parameter that defines the property and the applications of a particular cover of land. Ground water level data has been collected during the recent year (2018) across Punjab state for the analysis from India-WRIS (Water Resource Information System). The data was mapped using spatial analyst (krigging) of ArcGIS Software to improve the precision of spatial distribution mapping of groundwater level data (Anand et al. 2021) within the study area (https://gisgeography.com/kriging-interpolation-prediction/). Further the depth was classified in four classes (0–3 m; 3–10 m; 10–20 m and > 20 m) and the thematic map is shown in Fig. 8.

Fig. 8
figure 8

Spatial variation of groundwater depth (m) in the state

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2.4.2 Groundwater level fluctuations

Groundwater is the main and sometimes only source of irrigation to agriculture crops and any deviation in groundwater level impacts the yield, area and production of the crops. Agricultural operations get directly influenced by groundwater fluctuations and its production varies based on the growing season (Bhattarai et al. 2021). The data on the groundwater level fluctuations across Punjab during the recent decade (1998–2018) was collected from India WRIS (Water Resource Information System) site. It was mapped using spatial analyst (krigging) of ArcGIS Software to improve the precision of spatial distribution mapping of fluctuations in groundwater level data (Anand et al. 2021) within the study area (https://gisgeography.com/kriging-interpolation-prediction/). (https://gisgeography.com/kriging-interpolation-prediction/). and its thematic map is presented in Fig. 9.

Fig. 9
figure 9

Spatial variation in annual rise – fall of water table (m)in the state

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2.5 Methodology

In this study, to identify appropriate areas for cultivation of different crop types, climatic (minimum and maximum temperature and rainfall), soil (organic carbon, pH and texture) and groundwater (depth and level fluctuations) parameters were used. The methodology used is presented in Fig. 10.

2.5.1 Overlaying of the thematic maps

In the Geographic Information System (GIS) analysis the input multi-layered geospatial data is analyzed to derive insights and make informed decisions. In this study, GIS analysis was carried out in ArcGIS 10.2.2. The point shape file, integrated with the study area shape file, for each parameter across the study area was created by using the spatial data consisting of the latitude and longitude of each district and the attribute data consisting of the corresponding mean value were calculated and individual thematic maps for each parameter were developed using the Inverse Distance Weighted (IDW) interpolation method (Patel and Singh 2015).

2.5.2 Computation of weights consistency ratio

Analytic Hierarchy Process (AHP) is a multi-criteria decision-making (MCDM) technique that provides a structured method for decision-makers to evaluate and compare different alternatives based on a set of criteria or factors. It involves breaking down a complex decision problem into a hierarchy of sub-problems, where each level represents a different level of concept (Wart et al. 2013). The parameters considered were then compared pair wise with the help of a matrix in a tabular form wherein each of them was given a relative importance over another by using a numbers scale from 1 to 9 (Table 1), based on the priorities and then their weights are determined by the evaluation scale developed by Saaty in 1980 (Saaty 1980).The pair wise matrix is generally prepared based on the opinion of the experts (Ghorbanzadeh et al. 2018).

Table 1 Scale for extent of importance and its interpretation
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In the present study the parameters that were considered include minimum temperature, maximum temperature, precipitation, groundwater depth, groundwater level fluctuations, soil organic carbon, soil pH and soil texture. Out of these variables, minimum and maximum temperatures and rainfall have been given the greatest priority, whereas groundwater depth is given the lowest priority. The Analytical Hierarchy Process (AHP) was applied in four groups: AHP1, AHP2, AHP3 and Agro Eco Resource Zones (AERZ).

Fig. 10
figure 10

Flowchart of the methodology

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The AHP1 involved the thematic layers of minimum temperature, maximum temperature and rainfall while the AHP2 encompassed soil organic carbon, soil texture and soil pH. The AHP3 included groundwater fluctuations and groundwater table. Later the final Analytical Hierarchy Process (AHP) for Agro-Eco-Resource zoning was applied to AHP1, AHP2 and AHP3. Finally, a consistency check was done to ensure whether the original preference ratings were consistent or not. For calculating the consistency ratio (CR), the following procedures were undertaken: (1) The principal eigen value (λ) was calculated using the eigen value technique, and (2) the consistency index (CI) was derived using the equations given below:

$$Geometric\>mean = {\rm{\pi }}_{i = 1}^{\rm{n}}{{\rm{(}}{x_i}{\rm{)}}^{{\rm{1/}}n}}$$
(1)

Where, xi is the elements whose geometric mean is to be calculated and n is the number of elements

$$\:Normalized\:weights=\frac{\:Geometric\:mean}{\:Total\:mean}$$
(2)

For calculating the Consistency Ratio (CR), the principal eigen value (\(\lambda \max\)) and Consistency Index (CI) were computed (Kumar et al. 2019) as follows:

$$\lambda \max = {{\sum\nolimits_{i = 1}^n {{\rm{(}}P{\rm{)}}} } \over {{\rm{Parameters}}\,{\rm{in}}\,{\rm{row}}}}$$
(3)

Where, P denotes the sum of columns after calculation of pairwise matrix and normalized matrix.

$${\rm{CI}} = \>{{\lambda {\rm{max}}\> - {\rm{N}}} \over {{\rm{N}}\> - 1}}$$
(4)

Where N is the number of thematic layers used in the analysis.

The Consistency ratio (CR) is computed as:

$${\rm{CR = CI}}\,{\rm{ /}}\,{\rm{ RCI}}$$
(5)

Where RCI is the Random consistency index, whose values were obtained from the Saaty’s standard (Saaty 1980).

The pairwise matrices were generated for each group, considering their relative impact on agro-eco-resource zone with insights from relevant literature and expert evaluations (Table 2). The subclasses within each thematic layer were also reclassified in the GIS platform to assign weights ranging from 1 to 4 (Table 3).

Table 2 Pairwise matix for AHP1, AHP2 and AHP3
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Table 3 Pairwise matrix for AHP4
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2.5.3 Validation of agro-eco-resource zonation map

The zonation maps generated after AHP1, AHP2, AHP3 and AHP4 was then divided into four classes each. The first three thematic maps generated were: (i) climatic zoning map, (ii) soil zoning map and (iii) groundwater zoning map. These three thematic maps were then overlaid over each other as per the weighted criterion fixed for each input parameter and the final agro-eco-resource zonation (AERZ) map was prepared. In Punjab state rice-wheat is the dominant cropping pattern. The average yield of rice and wheat over the recent decade (2010-11 to 2019-20) for each district was collected from the Statistical Abstracts of the state. The average yield maps for the rice, wheat and rice + wheat was prepared. The validation of the AERZ map was done by comparing with these three productivity maps.

3 Results

3.1 Climatic zoning in the state

The climatic zoning map (Fig. 11) was generated by thematic layering of minimum temperature, maximum temperature and rainfall within the state. The map was divided into 4 classes, wherein the zone 4 had climatic conditions most suitable for all types of crops while zone 1 had climatic constraints and so was suitable for cultivating climate resilient crop.

Fig. 11
figure 11

Spatial variation of climatic zones for crop cultivation in the state

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3.2 Soil zoning in the state

The soil zoning map (Fig. 12) was generated by thematic layering of SOC, soil texture and soil pH within the state. The map was divided into 4 classes, wherein the zone 4 had soil conditions most suitable for all types of crops while zone 1 had constraints related to soil properties and so was least suitable for cultivating crops.

Fig. 12
figure 12

Spatial variation of soil zones for crop cultivation in the state

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3.3 Groundwater zoning in the state

The groundwater zoning map (Fig. 13) was generated by thematic layering of groundwater fluctuations and groundwater depth within the state. The map was divided into 4 classes, wherein the zone 4 had groundwater conditions most suitable for agricultural crops while zone 1 had constraints w.r.t. the groundwater and so was least suitable for cultivating crops.

Fig. 13
figure 13

Spatial variation of groundwater zones for crop cultivation in the state

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Fig. 14
figure 14

Spatial variation of agro-eco-resource zones based on two parameters for crop cultivation in different districts within the state

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3.4 Paired agro-eco-resource zonation based on two parameters

The paired agro-eco-resource zoning maps (Fig. 14) were generated by paired layering of two of the three (climate + soil, climate + groundwater and soil + groundwater) parameters. The maps with district boundaries were divided into 4 classes, wherein the zone 4 had conditions most suitable for agricultural crops while zone 1 had constraints and so was least suitable for cultivating crops.

3.5 Agro-eco-resource zonation in the state

The agro-eco-resource zoning map (Fig. 15) was generated by thematic layering of three (climate + soil + groundwater) parameters. The map with district boundaries were divided into 4 classes, wherein the zone 4 had conditions most suitable for agricultural crops while zone 1 had constraints and so was least suitable for cultivating crops. The geographical area under zone 3 was nearly 50% followed by zone 4 (30%), zone 2 (19%) and zone 1 (0.7%).

Fig. 15
figure 15

Spatial variation of agro-eco-resource zones for crop cultivation in different districts within the state

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3.6 Evaluation of the agro-eco-resource zonation (AERZ) map

The AERZ map prepared by the stepwise AHP’s was compared with the agro-climatic region (ACR) map prepared during 1980’s manually by integrating the agro-climatic and other natural resource data (Mavi 1984). In the ACR map the zone 3 (central plain) and 4 (western plain) regions are considered agriculturally most fertile regions of the state corresponded closely with the zone 3 and 4 of the AERZ map (Fig. 16). Traditionally in the state within the zone 5 (western region) climate resilient crops (Pearl millet, chickpea, pulses, etc.) were cultivated and this region corresponded to zone 2 and 1 of the AERZ map.

Fig. 16
figure 16

Comparison of the AHP zonation maps based on natural resources and manually generated map based on climate and geographical features within Punjab state

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3.7 Validation of the agro-eco-resource zonation (AERZ) map with crop yield

In Punjab state rice followed by wheat is the major cropping pattern practiced by the farmers. These two crops are very resource intensive crop and hence their yield is variable in different regions of the state. The AERZ map prepared by the stepwise AHP’s was compared with the crop yield maps by taking the average yield of rice and wheat over the recent decade, i.e. 2010-11 to 2019-20 in different districts (Anononymous 2023). The hypothesis for comparison was that yield of the two crops would be higher in regions falling in zone 3 and 4 of the AERZ map.

The comparison of AERZ map with the rice yield map revealed that high yield of rice (> 4400 kg/ha) was observed in major regions falling in zone 3 and 4 (Fig. 17). However in the northern and north western regions though falling within highly productive zone 3 and 4 of AERZ map lower rice yield (< 3400 kg/ha) was observed. This may be due to the undulating mountainous topography of northern districts (Pathankot and Gurdaspur) of the state. Moreover, in these regions (Pathankot, Gurdaspur, Amritsar, Fazilka and Tarn Taran) aromatic basmati rice is cultivated which have lower yield potential as compared to the parmal rice cultivated in other regions of the state.

The comparison of AERZ map with the wheat yield map revealed that high yield of wheat (> 4500 kg/ha) was observed in major regions falling in zone 3 and 4 (Fig. 18). However in the northern district (Pathankot) though falling within highly productive zone 3 and 4 of AERZ map lower wheat yield (< 4000 kg/ha) was observed. This may be due to the undulating mountainous topography of the district. The spatial variability in cumulative yield of rice + wheat when compared with the AERZ map showed that higher yields were observed in zone 3 and 4 of the AERZ map (Fig. 19). The undulating topography of the northern and north eastern regions of the state were the major cause of lower yield of rice-wheat cropping system alongwith the cultivation of aromatic basmati rice which has more than 50% lesser yield potential than parmal rice.

Fig. 17
figure 17

Comparison of the AERZ map with variability in rice yield within Punjab state

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Fig. 18
figure 18

Comparison of the AERZ map with variability in wheat yield within Punjab state

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Fig. 19
figure 19

Comparison of the AERZ map with variability in rice + wheat yield within Punjab state

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4 Discussion

4.1 Need for agro-eco-resource zonation in Punjab

Punjab state is a major contributor to food grain reserves of the country. The predominant rice-wheat cropping system with 76 and 85% area under rice and wheat, respectively (Anononymous 2023) is fast depleting the natural resources of the state. The groundwater table is depleting at a fast rate due to increase in number of tubewells from 1.92 lakh during 1970-71 to 14.76 lakh during 2020-21 (Anononymous 2023). Monsoon rainfall which contributes ~ 75–80% towards annual rainfall is showing a declining trend. A recent study by Sandhu and Prabhjyot-Kaur (2023) have reported that during recent two decades, 16 years received deficit monsoon rainfall. Consequently, the depth to water table > 10 m has increased from about 30% during 2000 to > 75% during 2019 (Sidhu et al. 2021). The climate system in the state is also witnessing changes such as increase in minimum temperature @ 0.03 to 0.06oC per year and relative humidity @ 0.2 to 0.3% per year while due to increase in cloudy weather conditions in the state, there is a decrease in evaporation @ 2 to 25 mm per year and sunshine hours @0.04 h per (Prabhjyot-Kaur et al. 2020). So, there is a need to characterize the zonation of agro-eco-resources in the state. During the 1980’s a characterization of agro-climatic zones was done manually by considering the agro-climatic and topographical characteristics (Mavi 1984). The state was subdivided into six agro-climatic zones with five land zones and one floodplain zone formed with the four major river basins, i.e. the Sutlej, the Ghaggar, the Beas and the Ravi.

4.2 Modern tools used in agro-eco-resource zonation (AERZ)

Nowadays the usage of modern tools such as GIS (Geographic Information System) and AHP (Analytic Hierarchy Process) is helping in providing a spatial overview of the natural resources such as climate (Geletič & Lehnert 2016; RajKumar et al. 2020), water (Uc-Castillo et al. 2022) and land resources (Tashayo et al. 2020). This information helps in understanding the crop and cropping-system suitability pattern for a given region.

Several studies have used AHP method in assigning the weight to important factors (Akinci et al. 2013; Zhang et al. 2015; Ghorbanzadeh et al. 2018; Tashayo et al. 2020). In the present study 8 major factors i.e. minimum temperature, maximum temperature, rainfall, groundwater depth, groundwater level fluctuations, soil organic carbon, soil pH and soil texture were considered for AERZ. The weight of the criteria was in the range of 0 to 1 (Malczewski and Rinner 2015) and those resulting from AHP is presented in Tables 2 and 3. The results showed that the value of the consistency ratio (CR) was 0.1 and hence were valid in accordance with the threshold recommended by Saaty (2008). The weighting results showed that the meteorological variables have the highest weight. This result indicates that meteorological variables i.e. minimum temperature, maximum temperature and rainfall are very crucial in the successful cultivation of agriculture crops. The next weights were soil organic carbon, soil texture and soil pH followed by groundwater fluctuations and groundwater depth. The weights assigned to the factors were integrated into ArcGIS 10.2.2 software and the thematic maps were created. In the present study zonation maps were generated after AHP1, AHP2, AHP3 and AHP4 and each map was then divided into four classes each. The class 1 corresponded to the category which was least favourable for agricultural crops, while class 4 was the category most suitable for agricultural crops (Yaghoubzadeh et al. 2020; Rajkumar et al. 2020).

4.3 Validation of the agro-eco-resource zonation (AERZ)

The innovative aspect of the present study was that it was evaluated and validated with the real time information on crop yield reported across different districts of the state during the recent decade (Anononymous 2023). The AERZ map created was evaluated by comparing it with the earlier developed agro-climatic zone (ACZ) map (Fig. 16). The comparison revealed the zone 3 (central plain) and 4 (western plain) regions in the ACZ are considered agriculturally most fertile regions of the state. Conclusively these two zones corresponded closely with the zone 3 and 4 of the AERZ map. The zone 1 and 2 of the AERZ map corresponded to the zone 5 (western region) of the ACZ map. In this zone due to lesser favourable agro-climatic resources, traditionally climate resilient crops (pearl millet, chickpea, pulses, etc.) had been cultivated prior to increased access to irrigation facilities.

The AERZ map as then validated with the spatially created yield maps of rice, wheat and rice + wheat crops during the recent decade in the state (Figs. 17, 18 and 19). The average yield of rice varied between 3400 and 4400 kg/ha, of wheat between 4000 and 5000 kg/ha and of rice + wheat between 4100 and 9300 kg/ha. The high yielding regions of the state were observed in the zone 3 and 4 of the AERZ map. In case of rice crop, lower yield was observed in north (Pathankot, Gurdaspur) and north western districts (Amritsar and Tarn Taran). These regions fall in zone 3 and 4 of AERZ map, but reason for low yield were the undulating topography in northern districts and cultivation of aromatic basmati rice which has lower yield potential than the parmal rice cultivated in other parts of the state. Since the aromatic basmati rice is cultivated in Fazilka district of the state, so lower yield was observed in this district too. In case of wheat crop low yield was observed only in Pathankot district which is having agro-eco-resources suitable for high yield of crops, but the undulating mountainous terrain is a major constraint which causes the lowering of wheat yield.

Similar type of validation of the zonation maps have also been done by in several studies. Hossain et al. (2007) identified the land areas suitable for tilapia farm development in Sitakunda Upazila in Bangladesh using GIS. The output of the study model showed the location and extent of tilapia farming areas on different suitability scales, i.e. most suitable (7744 ha), moderately suitable (2479 ha), and not suitable (838 ha). Then the model outputs were validated with field verification data, and the results were found consistent. Chowdary et al. (2013) conducted a comparison between the Analytic Hierarchy Process (AHP) based SYI model(AHPSYI) based sub watershed prioritization map with the prioritization map based on the observed sediment yield data, and observed that ~ 78% of the study area showed concurrence. Zolekar and Bhagat (2015) conducted a multi-criteria land suitability of the agricultural land in hilly zones of western ghats in Maharashtra state of India. They reported that within the study area ~ 17% (7326 ha) was classified in the class ‘highly suitable’, 29% (12,372 ha) in ‘moderately suitable’, 16% (6514 ha) in ‘marginally suitable’ and 38% (15,798 ha) in ‘not suitable’ for agriculture. Al-Taani et al. (2021) analysed the suitability of land for agriculture in Ma’an in Jordan. They reported that only 0.2% from the total area of Ma’an is highly suitable for rainfed agriculture, while about 1.4% is high suitable for the irrigated agriculture in the study area. Uc Castillo et al. (2022) conducted the validation for groundwater potential zones (GWPZs) created using AHP and GIS with the water residence time data of the existing tubewells in the region and reported an accuracy agreement of 67%.

Tashayo et al. (2020) used the GIS-AHP technique for analysing the land suitability for maize cultivation in Iran. The zonation revealed that within the region, 38.7, 26.9 and 23.9% of the area was highly suitable, moderately suitable and marginally suitable, respectively for maize cultivation. The remaining 10.5% area was not suitable for maize cultivation. The limitations for maize cultivation in the study area were validated with the prevalence of soil related constraints such as saline, sodic and calcareous materials in the Marvdasht plain soils in the region.

4.4 Suitable crop zonation for sustainable agriculture in Punjab

Agricultural land suitability analysis (ALSA) based on FAO (Food and Agriculture Organization) guidelines for sustainable crop production is important for attaining food security (FAO 1976, 2007). Akpoti et al. (2019) compiled a comprehensive review of 69 studies conducted for agricultural land suitability analysis (ALSA) for sustainable crop production in a region. Such type of work is important for attaining food security in the region. In the present study, 50% of total study area was classified in zone 3 (suitable for resource intensive crops), followed by 30% area in zone 4 (most suitable for resource intensive crops), 19% area in zone 2 (suitable for climate resilient crops) and 1% area in zone 1 (suitable only for climate resilient crops). To maintain the sustainability of the fertile and productive agriculture land of Punjab state, a reorientation of crop cultivation practices needs to be implemented. The proposed crop cultivation is given in Table 4. The cultivation of resource intensive crops (Rice/ Basmati rice, Wheat, Maize and Vegetables) should be curtailed to 30% of the total geographical area of the state. The agro-eco-resource constrained ~ 1% of the land area may preferably be kept fallow or used for forest cover. However, if it should be used for growing crops, then only climate resilient crops (Pearl millet, gram, fodder) may be cultivated. A diversified crop cultivation pattern wherein cereals (maize, wheat), oilseed (brassica, sunflower, groundnut), legumes (pulses), fibre (cotton) and vegetables is highly suitable for zone 2 and 3 in Punjab state. While using the AHP techniques for land suitability Yaghoubzadeh et al. (2020) concluded that 46.5% of the region understudy had high potential for saffron cultivation in Sarab, Iran. Land suitability analysis by using GIS and AHP by Topuz and Deniz (2023) reported that majority of the areas which should be used for pastures were being used as agriculture and forest areas in Demrici district in Turkey.

Table 4 Proposed suitable zonation for crop cultivation using AHP and GIS in Punjab
Full size table

5 Limitations of the study

The study primarily focus on physical and ecological criteria, potentially overlooking socio-economic factors that also impact agricultural land use decisions. The thematic maps sometimes simplify the nature of the site, so the variations on local scale may get generalized. Future climate change and dynamics of the nature of changes in land use on crop suitability are not explicitly addressed, which could affect the sustainability of agricultural practices in the long term.

6 Conclusion

Punjab’s land is considered one of the productive ecosystem in the world due to its rich soil and favourable climate conditions for crops. In addition the availability of water for irrigation from the canal networks and irrigation tubewell is a booster for agricultural production. However over time, both atmospheric and soil properties have been altered due to various factors. As a result, farmers in Punjab are facing numerous challenges in deciding which crops to grow in their areas for the highest yields and financial returns. The land suitability analysis for crop cultivation is being done over spatial scale using GIS and by overlaying the weighted layers using the AHP technique. The study demonstrates the integration of GIS and AHP as effective tools for agricultural land suitability analysis, contributing to precision agriculture and sustainable land management practices. It maps and ranks agro-ecological sub-regions into different regions on the basis of 8 major factors (minimum temperature, maximum temperature, rainfall, soil organic carbon, soil texture, soil pH, groundwater depth and groundwater level fluctuations) and identifies most and least favourable regions for planning interventions.

The results of the present study revealed that within the 50,362 sq.km geographical area of Punjab state, 15,289 sq.km (~ 30%) is suitable for cultivation of resource intensive agricultural crops (zone 4), followed by 24,991 sq.km. (~ 50%) falling in zone 3 which is suitable for agricultural crops and 9726 sq.km. (~ 19%) falling in zone 2 which is suitable for less resource intensive crops. The practical significance is highlighted by the fact that the crop suitability maps have been validated by the actual agricultural yield data, thereby strengthening the empirical basis our assessment. Hence, the state is rightly referred to as “Food Bowl” of the country as only 356 sq.km. (~ 0.7%) area is having agro-eco-resources suitable only for climate resilient crops. It may be concluded that for sustainable agriculture in the state, a critical appraisal of the crop cultivation roadmap needs to be undertaken for strict implementation.