Introduction

Ecosystems are the surface of an ecological hierarchy in which key processes such as water, carbon, nutrient, and primary production cycles are formed and measurable (Tilman et al., 2002). These processes form the basis of ecosystem services (ESs) (Ghermand et al., 2012). Ecosystem services are expressed as a set of tangible values in human society that are the result of the quality or quantity of a natural asset (TEEB, 2010). They are very diverse and have ecological, social, and cultural dimensions. Determining their value in priced form is the easiest way to inform the community and policymakers about their importance.

Ecosystem services act as a bridge between ecosystem structures, processes, and human well-being. The economic value conceptual framework views ecosystem goods and services as the flows of benefits to humans. Values of services are assessed through the ways ESs support people’s own consumption and use intangibles (non-use values). Valuing ESs can be used to assess changes in the quality of ecosystems, payment for ESs policies, and to promote ecosystem protection and ecological civilization construction (Nan et al., 2021).

Ecosystem services are valuable direct or indirect benefits to humans that provide a natural environment (Nelson et al., 2009). These are often categorized as provisioning, regulating, supporting, and cultural services. It can be revealed that important parts of ecological processes are actually regulating services which are essential for life support systems (MEA, 2005). The abilities of natural and semi-natural ecosystems to regulate essential ecological processes and life support systems are related to this group of services (Kreuter et al., 2001). There are different frameworks to classify ESs. One of them is the Common International Classification of Ecosystem Services (CICES). CICES recognizes three main sections of ecosystem outputs: provisioning, regulating, and cultural services (Haines-Young & Potschin, 2018).

One of the most important steps in quantifying ESs is mapping the services. ESs maps provide valuable information about the quality of ecosystems for decision-making (Willeman et al., 2010). Mapping can also be used to predict community needs for ESs under land use change (Daily, 2009). It is a powerful tool for transmitting the values of land use to policymakers and decision-makers. Costanza et al. (1997) produced the first ESs maps, and approximately 60% of ecosystem service mapping has been conducted since 2007 using different methods for mapping. In recent years, using GIS in the mapping of ESs has developed very well (Maes et al., 2012). ESs mapping provides fundamental insights into the spatial characteristics of the flows of goods and services from nature to human society. It has become a major issue in science, politics, business, and society-all belonging to functioning ecosystems (Burkhard & Maes, 2017). The maps have important contributions to the applications of the ESs approach in science as well as in practice. Today’s demand and consumption of ESs is far from the actual supply. Maps can help visualize this mismatch. Certainly, the demand side has been neglected in most studies of ESs thus far, perhaps because data on demands is more difficult to obtain (Burkhard et al., 2012). Bicking et al. (2020) quantified and mapped the nutrient regulation ESs demand in Germany. Their results showed that crop rotation systems lead to reducing nitrogen budgets and common agricultural practices lead to increasing nitrogen budgets. In another study, Hinsch et al. (2024) showed that the state of the ecosystem and type of land use were important parameters to show the suitability of the habitat for pollinators. Especially for ecosystem types with different habitat suitability, such as agricultural ecosystems, the implementation of ecosystem status parameters is recommended. In the State of New Hampshire, USA, Mikhailova et al. (2021) investigated the vulnerability of soil carbon regulating ESs due to land cover change. Their findings showed that spatial and temporal land cover analysis could identify crucial locations of soil carbon regulating ESs that are at risk. In other research, Morri et al. (2014) studied and mapped the carbon sequestration at different altitudes. They concluded that carbon sequestration was directly related to altitude, but demand for carbon sequestration was inversely related to altitude.

Baker et al. (2021) mapped regulating ESs in deprived urban areas in Manchester. The results showed that mapping as a transferable city-wide visualization tool, using accessible data and methods, investigates regulating services in deprived urban areas. Sieber and Pons (2015) identified the weighted and prioritized the functions of the ecosystem in Singapore’s city using expert opinion. Their research showed that water protection and regulation functions had higher priority among other parts, respectively.

Blanco-Canqui (2024) reported that the incorporation of prairie strips, agroforestry, grass buffers, organic systems, and cover crop into croplands can increase ESs such as accumulate soil carbon, improve soil biodiversity, reduce soil erosion and nutrient losses, and contribute to climate change adaptation. Overall nature-based solutions based on perennial vegetation offer more promise than those based on annual crop. In another study, Wang et al., (2024) carried out a case study in Central Asia for assessment of future multiple ESs. Their results showed that with the increase in carbon emissions, the terrestrial carbon storage and water yield will first increase and then decrease in 2050. Also, habitat suitability and soil conservation experience a significant decline under indicating environmental risks associated with high-emission.

Regulating services are the advantages that humans receive from regulating ecosystem processes. Quantifying and mapping ESs in agroecosystems is one of the main effective actions to increase pay attention to these services and adopt suitable approaches to direct sustainability. Canola is now available on a wide range of fields around the world in the rotation and is cultivated with different crops, especially cereals. Due to the importance of canola in Golestan province and Iran, quantification and mapping of various services can clarify the current status of ESs in the canola agroecosystems, suitable management strategies regarding the optimum and efficient use of ESs, protection of services as offer a platform for sustainable development and increasing service delivery instead of service consumption. Canola can provide an important role in Golestan province regarding soil fertility, nutritional needs, and economic benefits by providing ESs. But, few studies have been done on the evaluation of ESs in the agroecosystems of Iran. There is a little information about the status of service provision in canola ecosystems. Also, the effects of between field management on the status of ESs are not clear in these agroecosystems. Therefore, the purpose of this study is to quantify and mapping of regulating services in canola agroecosystems using field surveys, facilitate laboratory experiments, and spatial–temporal analysis using a Geographical Information System (GIS).

Materials and methods

Study area

This study was carried out in the Sorkhankalateh region of Gorgan County, north of Iran. Sorkhankalateh region is located in the northeast of Gorgan County in Golestan province and latitude 36° 91′ 25″ to 36° 54′ 45″ north and longitude 54° 59′ 61″ to 54° 35 ′46″ east (Fig. 1). It is located in a flat plain and with heavy rainfall in autumn, winter, and early spring. In July and August, the weather in this area is hot and humid. The average and minimum annual temperature is 27 and 1.3 °C, respectively. The climate of the region is semi-temperate according to the De Martonne’s classification (Mohammad Zamani et al., 2007). Due to favorable climatic conditions, Sorkhankalateh has the necessary potential to produce some crops such as canola, potatoes, wheat, barley, corn, faba bean, and soybean. From the point of view of the physiographic unit, the Sohrkhanklate area is one of the Piedmont plains and its parent materials are of loess origin. According to the American classification, the soil of the region includes fluventic, mesic, mixed, fine, and haploxerepts (Soil Survey Staff, 2003).

Fig. 1
figure 1

Location of the study area and sampled canola fields, Golestan province, Iran

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Golestan province has the first rank in terms of canola cultivation area in Iran, with 50,983 hectares and 32.3% of the canola cultivation area of the country. The amount of canola production in Golestan province was 109,175 tons, which included 31.1% of the total canola production in Iran in 2020. Also, the canola cultivation area in Gorgan County is 5525 hectares, and its production amount is 9417 tons, of which the Sorkhankalateh region shares 704 hectares and 1,410 tons of total production in Gorgan County (Agricultural Organization of Golestan Province, 2020).

Framework of evaluation and quantification of services

Quantification and valuation of regulating services in canola agroecosystems were performed according to “The Common International Classification of Ecosystem Services (CICES)” framework. The CICES is a reference classification system used in the mapping and assessment of ecosystems and their services, which is an internationally recognized tools for integrating knowledge on ESs in policy and decision-making processes (Heink et al., 2016; Maes et al., 2016). In this study, a quantitative system, based on field surveys, laboratory experiments, and spatial analysis, was used to evaluate regulating services in canola agroecosystems. We modified the CICES framework for use in a canola agroecosystem and evaluated the ESs accordingly. Based on the CICES framework, the regulating service assessed by some indicators such as carbon sequestration, organic carbon, organic matter, oxygen production, soil microbial respiration, soil aggregate stability, and pollination (Fig. 2).

Fig. 2
figure 2

Framework for quantification and mapping of regulating services in canola agroecosystems based on CICES

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Data gathering

Sampling from 50 canola fields was carried out in two stages of the growing season, before sowing and after crop harvest, using an auger from a depth of 0 to 30 cm of soil (Fig. 1). Field sampling was done according to the W-shaped pattern. We recorded the geographical coordinates of the sampling location by a GPS, Garmin Touch model for each field.

A part of the required data was collected through face-to-face interviews with the questioners and field visits. The number of studied fields was considered using Cochran’s formula (Eq. 1) (Snedecor & Cochran., 1989):

$$n=\frac{{N(s\times t)}^2}{\left(N-1\right)d^2+{(s\times t)}^2}$$
(1)

In this equation, n is the number of samples, N is the number of the statistical population (all farmers), s is the estimated deviation of the society, and t is the value of the normal scale of the standard unit. The allowed error value is considered to be 0.05. A number of questions about agricultural practices such as tillage, fertilization, irrigation, pest control, weed control, crop rotation, consumption of manure, etc., were included in the questionnaire.

Services quantification

Soil organic matter

In this study, Walkley and Black method (1934) was used to measure soil organic carbon. The Walkley–Black procedure is considered to be cheap and easy to perform. Some researchers confirmed this method in terms of suitability, economical benefit, time efficiency, and environmentally friendly (El Mouridi et al., 2023; Wang, et al., 2012). In this method, first, the sampled soils from the fields were mixed and air-dried in the shade to remove the moisture of the samples completely. The samples were then pounded and passed through a 1 mm sieve. Then, 0.5 g of the crushed sample was poured into Erlenmeyer, and 10 ml of one normal potassium dichromate was added to it. Erlenmeyer was shaken well to dissolve the sample entirely in dichromate. After this step, 20 ml of concentrated sulfuric acid was added. The samples were then set aside for 30 min. Then, 150 ml of distilled water was added to the samples and set aside to cool completely. After cooling, five drops of Orthophenanthroline monohydrate reagent were added to each sample and titrated to 0.5 normal using Ammonium iron (II) sulfate. A soilless control sample was also measured for each group of samples. Finally, using Eq. (2), (Nelson & Sommers, 1996), the amount of soil organic carbon was calculated.

$${\%}OC=M\times 0.39\times ((\frac{V1-V2}{\text{S}}))$$
(2)

% OC is the percentage of organic carbon, M is the normality of ammonium iron (II) sulfate, V1 is the ammonium iron (II) sulfate used for control (ml), V2 is the ammonium iron (II) sulfate used for the sample (ml), and S is the weight of dry air sample (gram). After calculating organic carbon, the amount of soil organic matter was calculated using Eq. (3) (Nelson & Sommers, 1996).

$${\% }OM={\% }OC\times 1.72$$
(3)

where OM and OC are the amount of soil organic matter and amount of organic carbon, respectively.

Soil carbon sequestration

To estimate the amount of soil carbon per unit area, sampling was taken before cultivation and also after crop harvesting, and the samples were then passed through a 2-mm sieve after transfer to the laboratory and until chemical decomposition, stored at room temperature as air dried and the amount of soil organic carbon was estimated by Walkley and Black (1934). Then, the bulk density of the soil was calculated (Marshall et al., 1999). Finally, Eq. (4) was used to calculate the amount of carbon sequestration (kg per ha).

$$SCS = SO\times BD \times H \times 10$$
(4)

where SCS is the soil organic carbon storage (kg per ha), SOC is the amount of carbon measured by the Walkley and Black method (gram per kg), BD is the soil bulk density (gram per cm3), H is the thickness of the soil layer (m), and 10 is the coefficient for converting kg per ha. In this study, soil bulk density (BD) was determined by the Marshall et al., (1999) method. The amount of organic carbon was measured in two sampling stages, and the difference between these two stages determined the amount of carbon deposited during the canola growing period (Eq. 5) (Li et al., 2016).

$${CS}_{eqsoil }={SOC}_{after}-{SOC}_{before}$$
(5)

where CSeqsoil is the soil carbon sequestration, SOCafter is the organic carbon storage in the soil after harvest canola, and SOCbefore is the organic carbon storage in the soil before sowing of canola.

Measurement of released oxygen

Based on net primary production in the photosynthesis equation, 1.63 kg of carbon is stabilized per kg of dry matter production (Li and Ren, 2014). Also, according to the photosynthesis Eq. (6), plant species absorb 180 g of glucose and 193 g of oxygen, 264 g of carbon dioxide, and 108 g of water to produce. In other words, to produce 1 kg of dry matter, 1.2 kg of oxygen is released. Using the formula of photosynthesis and by measuring the biomass of plants and determining their dry weight, the amount of oxygen released can be calculated using Eq. (7) (Li and Ren, 2014).

$${H}^{2}O\left(108 g\right)+C{O}^{2}\;\left(264 g\right)\stackrel{6772 calorie solar energy}{\to }Glucose\left(180 g\right)+O2(193 g)\to Polysaccharide (162 g)$$
(6)
$$O2\;supply\;(kg/ha)\;=\;total\;dry\;matter\;(kg/ha)\times\;1.2$$
(7)

Soil microbial respiration

Anderson’s method was used to measure soil base respiration (Anderson, 1982). In this method, 10 g of moist soil was placed in a centrifuge tube and in a more oversized glass containing 20 ml of 0.5 M NaOH. The glass door was then tightly closed so that no air exchange occurred between the inside of the glass and the outside space; the container was then stored at 25 to 28 °C for 72 h; after, 72 h, the centrifuge tubes containing the soil were removed from the larger container. Then, 2 ml of 0.5 M BaCl2 solution was added to each sample to precipitate the absorbed carbon dioxide by NaOH as BaCO3. Then, five drops of phenolphthalein were added and titrated with 0.1 M HCL and the remaining NaOH. When titrated by adding phenolphthalein reagent to the sample, the sample color turns purple to pink, which is light yellow as soon as the titration is finished, and the amount of acid consumed is noted at the end. For each soil group, a sample was considered a control. A control glass sample containing 20 ml of 0.5 M NaOH was prepared without soil. Then, the microbial respiration rate was calculated using Eq. (8) (Anderson, 1982). Finally, it was divided into 3 (number of test days) for daily calculation.

$$(\frac{\left(\text{C}-\text{S}\right)\times 2.2\times 100}{\textrm{SW}\times \%\textrm{dm}})/\text{d}=\text{m gram CO}2.\text{Kg}.\text{day}$$
(8)

where C is the average volume of HCl used by control treatment (mL), S is the average volume of HCl used by samples (mL), 2.2 is the conversion coefficient; SW is the primary soil weight (gram), d is the total days of the experiment (3 days), and 100/%dm is the conversion coefficient for dry soil.

Soil protection

Suitable soil aggregation is important to improve soil fertility and quality. Usually, the stability of aggregate is used as an indicator of the structural stability of soils. Various indices have been proposed for expressing the distribution of aggregate sizes. One of the methods for soil building stability to investigate the size distribution of soil aggregates is the wet sieve method. There are various indicators for expressing aggregate size distribution; one, of these indicators is the mean weight of diameter (MWD), which expresses the arithmetic-weight average of the size of stable aggregates. MWD is a widely used index to integrate aggregate size distributions obtained by mechanical sieving. MWD measurements show an important variation under different cropping and tillage practices (Tisdall & Oades, 1982). In this study, to determine the aggregate stability, the wet sieving method and wet sieving apparatus device made by the Ejke Kamp company were used. In this method, the first 10 g of soil from each sample of treatments with a 4 mm diameter was weighed, and a series of sieves with sizes of 2, 1, 0.5, 0.25, and 0.125 mm were used. The sum of the sieves was moved in distilled water at a speed of 34 rpm for 10 min at a fluctuation of 1.3 cm. The number of remaining particles on each sieve after drying in the oven (at 105 °C) was then weighed (Kemper & Rosenau, 1986). In the last step, by placing the results obtained in (Eq. 9), the mean weight soil diameter for each sample was determined.

$$MWD=\sum_{i=1}^{n}Xi Wi$$
(9)

where MWD is the mean weight diameter of water stable aggregates, Xi is the mean diameter of the remaining aggregates on the sieve (mm), and Wi is the proportion weight of aggregates remaining on each sieve to the total sample mass.

Pollination

Pollination is a critical process in natural and agricultural ecosystems. Hence, pollination has an essential role in those human foods production and livelihoods, which directly depend on natural ecosystems and agricultural production systems. In this study, to collect incest pollinators we used a standard entomological net (hoop diameter 40 cm and handle length of 120 cm). Sampling was done randomly in two stages (from mid-March to late April) at several different points in the fields. Sampling was performed at the peak of insect activity and in favorable weather conditions (sunny weather and lack of strong winds) from 8 am to 3 p.m. After collecting insects, honey bees were first counted and released. Other collected insects were killed in a killing jar using a piece of cotton saturated with chloroform (70%) in polyethylene pages and were transferred to the laboratory. Collected samples after segregation, using special needles or pinned points with sizes 00, 02, and 01, were labeled. All insect specimens were identified by the fourth author.

Mapping of ecosystem service

After collecting the data to create a database of regulating services, first, the data were transferred from GPS format to Arc GIS software version 10.3 (ESRI, 2011). All spatial datasets were georeferenced to UTM (WGS-84) coordinate system, and the spatial distribution of all ES was interpolated in ArcGIS software version 10.3. From all the consistently obtained information, create a database with a spatial location, and after separating the data, the map of each regulating service was produced using the interpolation methods such as inverse distance weighted, kriging, and local polynomial interpolation and based on the minimum of root mean square error (RMSE) and general standard deviation (GSD).

Statistical analysis

In this study, two canola cultivars which are called Hayola 50 and Trapper were cultivated by farmers in surveyed fields. Based on it, we analyzed all data by T-test in SAS statistical software, version 9.3 (SAS Institute, 2000). Also, data normalization was performed using the Kolmogorov–Smirnov test.

Results and discussion

Climatic service—carbon sequestration

It was observed that the carbon sequestration amount was negative in some canola fields, and this indicates that the release of carbon was higher than carbon accumulation during the growing season of crop. This result can be due to the activity of soil microorganisms, especially when the residue remains on the soil surface. Also, there was no significant difference in carbon sequestration rate in the two cultivars of Hayola 50 and Trapper cultivars (Table 1). Based on field-measured data, Zhang et al. (2017) approved the higher emissions (14,857.3 kg CO2 eq ha−1) for corn cultivation in China, because of mechanized cultivation and the excessive use of chemical fertilizers. Also, Camargo et al. (