Vulnerability Assessment of Potato Growth to Climate Change Based on GIS in Inner Mongolia, China

Abstract

Since 2016, the potato has gradually become the fourth major staple food in China, and the potato planting area and total output in Inner Mongolia rank among the top in the country. Potato is a climate dominant crop in Inner Mongolia, and it is an urgent requirement to study the impact of the potato’s climate vulnerability and effectively avoid climate risks to ensure national food security. An index system for a vulnerability assessment of potato production in Inner Mongolia was established based on GIS and AHP. Based on the definition of vulnerability and the theory of disaster risk, a comprehensive evaluation model of potato growth vulnerability was established. The results showed that the potato production in central Inner Mongolia was highly vulnerable, while the potato production in eastern and western Inner Mongolia was relatively vulnerable. Central Ulanqab, southern Hohhot, southern Baotou and southwestern Xilin Gol League were most vulnerable. The eastern part of Hulunbuir, Xingan League, Tongliao City and the southern part of Ordos City are the least vulnerable areas, while Chifeng, Bayannur and most other parts of northern Ordos City are moderately vulnerable areas. According to the different influencing factors of climate change vulnerability in major potato producing areas, different countermeasures should be taken respectively. The results can provide a scientific basis for the sustainable development of potato production in autonomous regions. The research results were approved by the national Potato Meteorological Service Center.

1. Introduction

The assessment of global climate change and its impacts has become one of the most active research fields in the international academic community [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]. Climate change has seriously changed the spatiotemporal climate pattern in China, resulting in significant changes in the spatiotemporal pattern of agricultural climate resources in China [27], increasing the instability of agricultural production and the risk of reduced food production. It has become necessary to assess the vulnerability impact of climate change on agricultural production from a professional perspective. Since 2016, potatoes have become the fourth major staple food in China, which can benefit national food security. It is urgent to study the vulnerability impact of climate change on potato growth to promote the country’s food security strategy. Inner Mongolia is the largest potato production base in the country, and the planting area and annual yield rank among the top three in the country, accounting for more than 10% of the country’s totals. Inner Mongolia has basically formed a Ulanqab potato industrial advantage zone in central China and a potato industrial advantage zone in eastern China, with the city of Hulunbuir as the center [28,29]. In particular, Midwestern Sectional Figure Skating Championships potatoes are better adapted to the local geography and climate than other crops, and they play an extremely important role in production. It is urgent that we understand the vulnerability impact of climate change on potato production and take countermeasures against production threats. To this end, we collected comprehensive meteorological and potato growth period observation data in Inner Mongolia for the past 30 years. Based on the IPCC [30,31,32,33] definition of vulnerability, we built a comprehensive assessment model of potato vulnerability to climate change from two aspects—sensitivity and adaptability—and quantitatively assessed the impact of climate change on potato production vulnerability in Inner Mongolia. The results can provide a scientific basis for the sustainable development of potato production. It is of great significance to formulate potato planting plans reasonably, avoid risks, improve the utilization rate of climate resources, formulate agricultural insurance plans, improve disaster prevention and reduction ability, ensure national food security and cope with climate change.

Most researchers, in China and abroad, have studied the vulnerability of agroecosystems [34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]. For example, Sun Fang [44] used crop models and climate models to quantitatively assess the vulnerability of farmland to climate change; Lin Erda and Wang Jinghua [45] made a quantitative assessment of the national agricultural vulnerability and classified the climate-vulnerable agricultural areas in China. Liu Wenquan [46] studied the impact of agricultural climate change in the Loess Plateau region and achieved the expected results. He Lei, Tang Wei‘an and Liu Jie [47,48,49] assessed the climate vulnerability of agricultural production in China’s agropastoral transition zone and in the Ningxia and Jiangxi regions. But relatively few studies have focused on the vulnerability of a single crop [50]. Among these studies, Yang Xiu [51,52,53] and others evaluated the climate sensitivity and vulnerability of maize, rice and wheat from the perspective of crop yield variability. Dong Zhiqiang [54] evaluated the vulnerability of wheat to climate change in Inner Mongolia, but the common characteristic of these studies was that their selected indices were relatively singular and their methods were limited. On the basis of previous studies, this project has preliminarily realized a comprehensive vulnerability impact assessment of multiple factors and indicators. In particular, changes in the climatic suitability of light, temperature and water were used to characterize the sensitivity of potato production to climate change; this approach more closely characterizes the dynamic changes in the demand for climate factors by the crops themselves, which is an improvement over most previous assessments of the impacts of agricultural vulnerability that were based solely on climate variability.

2. Data

Meteorological data were obtained from the Inner Mongolia Meteorological Bureau. From 1961 to 2019, the daily mean temperature (°C), daily precipitation (mm), sunshine hours (h), frost-free days and other data were selected from the nearest meteorological observation station. The agricultural meteorological observation data included the potato growth period at 11 Inner Mongolia stations (Figure 1) in the past 30 years, and the potato yield and area in 82 counties. Socioeconomic data, including total population; area; unit yield; total yield; farmer income; nonagricultural gross output value and per capita GDP; and mechanized agriculture and fertilizer application were obtained from the statistical yearbook. The reclamation index was calculated on the basis of the abovementioned relevant data. For remote sensing data, the effective irrigation area ratio was based on secondary data extraction statistics. Disaster data and soil and water loss data from Inner Mongolia were obtained from the soil and water conservation monitoring station of the Department of Water Resources; these reflected the results of the 2011 and 2018 soil and water loss surveys in Inner Mongolia. Disaster loss was calculated according to the evaluation model of Zheng Jingyun [55]. Other data, including temperature suitability, precipitation suitability, sunshine suitability according to the ground meteorological observation data and the agricultural test station observation data, were based on a quantitative assessment model and an evaluation index of the climate impact during the potato growth period.

The Inner Mongolia Autonomous Region is one of China’s 13 key grain-producing provinces (regions). As of 2017, this region has 9.271 million hectares of cultivated land, accounting for 7.8% of its total area, and the per capita cultivated land area is 3.7 times that of the whole country. The effective irrigation area is 3.175 million hectares. The soil in this area shows obvious meridional differentiation. Black soil, dark brown soil, chernozem, chestnut soil, brown calcium soil, brown desert soil and gray desert soil are distributed from east to west [56]. Cinnamon soil, calcareous soil, meadow soil and aeolian sandy soil are distributed in some areas. The soil organic matter (9.75~0.56%) [56] and clay particle (12.41~3.65%) [57] contents gradually decrease from east to west, while the soil pH value (7.39~8.90) gradually increases from east to west [57]. The soil is mostly neutral and alkaline [57,58]. Inner Mongolia covers a vast territory, with large east–west and north–south spans, complex landforms (e.g., plateaus, mountains, hills and plains), and special geographical locations and landforms, which form complex and diverse climate conditions that are dominated by a temperate continental monsoon climate, with varied temperature, rainfall and heat waves. Within the growth season, the climate is cold, which makes the potato, which prefers cold and cool habits, the main local crop. Popular potato varieties among the local farmers mainly include Kexin 1, zihuabai, shapoti, feiniarita and houqihong [59].

3. Research Methods

3.1. Data Standardization

To eliminate the influence of the dimensionality of the data, the range of the data is standardized. The formula is as follows:

$$I_i=0.5+0.5\frac{I-I_{\min }}{I_{\max }-I_{\min }}$$

(1)

In the formula, $I_i$ is the standardized range data, $I$ is the original data, and $I_{\max }$ $I_{\min }$ are the maximum and minimum, respectively, in each column.

3.2. Calculation of the Sensitivity and Adaptability-Weighted Comprehensive Evaluation Method

The weight coefficients are determined by the analytic hierarchy process (AHP) and expert scoring method, and then multiplied by the quantitative values of each index of the evaluated objects. The risk of disaster-causing factors, the vulnerability of disaster-bearing bodies and disaster prevention and the reduction ability index are all established by a weighted comprehensive evaluation method. The formula is:

$$RSI={\displaystyle \sum _{i=1}^n{\mathrm{X}}_i{W}_{\mathrm{i}}}$$

(2)

In the formula, $RSI$ is the sensitivity index, ${\mathrm{X}}_i$ is the quantization value of the first index of sensitivity ($0\le {\mathrm{X}}_i\le 1$),  is the weight coefficient of the first index of sensitivity (${\mathrm{W}}_i$ > 0, ${\displaystyle \sum _{i=1}^n{W}_{\mathrm{i}}}=1$), the weight coefficient is determined by the analytic hierarchy process (AHP) and expert scoring method, and n is the number of sensitivity indices.

$$RAI={\displaystyle \sum _{i=1}^n{\mathrm{Y}}_i{W}_{\mathrm{i}}}$$

(3)

In the formula, $RAI$ is the fitness index, ${\mathrm{Y}}_i$ is the quantization value of the first index of sensitivity ($0\le {\mathrm{Y}}_i\le 1$),  is the weight coefficient of Index i of adaptability ($W_{\mathrm{i}}$ > 0, ${\displaystyle \sum _{i=1}^n{W}_{\mathrm{i}}}=1$), the weight coefficient is determined by the AHP and expert scoring method, and n is the number of adaptability indices.

3.3. Vulnerability Index Calculation Ratio Method

The composite climate change vulnerability index is calculated using the following formula:

$$RIC=RSI/RAI$$

(4)

In the formula, $RI\mathrm{C}$ is the vulnerability index, $RSI$ is the sensitivity index and $RAI$ is the adaptability index.

4. Construction of the Indicator System

4.1. Description of the Indicator System

The concept of vulnerability, indicating the degree of injury, originated in the field of disaster research and is mainly used to analyze food security and famine. In its third assessment report, the IPCC stressed that vulnerability refers to the degree to which the system is vulnerable and has the capacity to cope with the (mainly adverse) impacts of climate change, including climate variability and extreme weather events. The former refers to the sensitivity of the system to climate change, and the latter refers to the adaptation of the system to climate change’s effects. Therefore, the vulnerability assessment of agricultural production to climate change includes a sensitivity assessment and an adaptation assessment. The sensitivity evaluation reflects natural and endogenous factors that affect the degree of vulnerability of agricultural production, while the adaptability evaluation reflects the socioeconomic factors that affect the degree of vulnerability; using both evaluations helps better assess the vulnerability of agricultural production to climate change [1,32].

4.2. Selection Principles of the Indicators

According to the characteristics of the research object, an evaluation index is selected by referring to previous research results combined with experience.

Principle of comprehensiveness: All kinds of influencing factors and expression methods are considered to establish a multi-index system for comprehensive analysis and evaluation, so that both individual and comprehensive analyses can be performed. This is because the agricultural system is very complex, its vulnerability is affected and restricted by many factors, and there are complex, nonlinear interactions among these constraints; therefore, establishment of a reasonable index system for comprehensive evaluation is essential [60,61,62,63,64].

Principle of dominance: The main factors affecting the vulnerability of potato production under climate change are highlighted. This project assumes that a change in climate suitability is the most influential factor, including the change in the suitability of light and warm water in each growth period, and calculates the suitability of light and warm water during the whole growth period compared to the weighted average. In this way, the change trend of climate suitability in the last 20 years is calculated as the main index of climate sensitivity evaluation.

The operability principle: A quantitative assessment of vulnerability is conducted on a qualitative basis, in which specific factors with high impact, sensitivity and operability values are selected as indicators for the sake of simplicity and feasibility [58,59,60].

4.3. Primary Selection and Screening of Indicators

To collect and analyze the literature on vulnerability assessment in China and abroad and to combine land use, agricultural production environment and socioeconomic status data in Inner Mongolia, 24 relevant assessment indicators were collected and classified according to sensitivity and adaptability, and an Alternative Index database was constructed. Based on the actual situation observed in the autonomous region, the availability of the index data and the main principles of constructing the index system, a statistical analysis was carried out according to quantitative and qualitative information obtained by consulting experts. Two rounds of expert consultation were used to screen alternative indicators, and finally, a vulnerability assessment index system of potato production under the influence of climate change in Inner Mongolia was constructed (Figure 2). The vulnerability index system consists of two element layers: sensitivity and adaptability. The sensitivity index is composed of two types of first-class indices: the climate sensitivity index and the land use index. The climate sensitivity index includes five secondary indices: temperature suitability, precipitation suitability, sunshine duration suitability, frost-free periods and disaster loss. There are two secondary indices of land use: the soil erosion rate and the reclamation index. The adaptability index consists of two types of first-class indicators: the agricultural production index and the socioeconomic index. There are six secondary indicators in the agricultural production category: the ratio of effective irrigated area (crops), per capita cultivated area of potato, per capita potato yield, per unit yield of potato, total mechanized agricultural power and fertilizer application. The socioeconomic indicators include three secondary indicators: per capita net income of farmers, nonagricultural gross output value and per capita GDP.

4.4. Connotation of the Evaluation Index

Vulnerability assessments of potato production to climate change, which are of major concern, include factors and indicators that are more sensitive to climate change impacts on agricultural production processes and the extent to which local socioeconomic, agroecological and agricultural production conditions are resilient to climate change; vulnerability to climate change is evaluated by combining two sets of indicators.

4.5. Determining the Weight of the Evaluation Index

Presently, the expert grading method and the analytic hierarchy process (AHP) are commonly used [50]. The expert scoring method involves a questionnaire distributed to several experts to consult with them on the weights of the agri-climate vulnerability indicator system. This method synthesizes the scores that the experts assign to the weights of each indicator and repeatedly modifies the weights and re-consults with the experts until an agreement is reached among the majority of experts. The AHP is a qualitative and quantitative analysis method that is often used to solve multiobjective, multicriteria, multifactor, multilevel and unstructured complex problems with decision making. By modeling and quantifying the decision making process to resolve complex problems, these problems are decomposed into several related and ordered levels. Each level involves several factors, and the relevant elements of each level are compared and judged. The relative importance of each factor is quantified, and then the importance order and weight of all factors are determined by mathematical methods.

5. Evaluation Result

5.1. Sensitivity Evaluation

5.1.1. Climate Sensitivity Assessment

According to the grid calculations of the five factors (temperature suitability variation; variation of precipitation suitability; variation of sunshine suitability; frost-free period change; and changes in disaster losses, corresponding to C1, C2, C3, C4 and C5, respectively), the evaluation results of climate sensitivity are obtained (Figure 3). The potato production in the southwest and northeast of Chifeng City; the middle of Xilin Gol League; the south of Hohhot; Baotou and Bayannur; and the north of Ordos and Wuhai City were more sensitive to climate change. Among them, potato production in western Inner Mongolia is highly sensitive to the climate due to high temperatures and less precipitation and sunshine, and some areas are highly sensitive due to large amounts of disaster damage.

5.1.2. Evaluation of Land Use Sensitivity

Based on the grid calculations of the two factors of soil and water flow rate and the reclamation index, the high-value areas of land use sensitivity were in Hohhot, Baotou, Ulanqab and western Xilingol League (Figure 4).

5.1.3. Sensitivity Evaluation

According to the weights determined in

Table 1. Factor weight coefficient of each inÞx layer.

RSI/RAI.	Level 1 Indicators	Level 1 Weight	Secondary Indicators	Unit	Secondary Weight	Total Weight
Sensitivity A1	Climate Sensitivity B1	0.80	Temperature suitability variation (C1, Negative)	Dimensionless	0.30	0.24
			Variation of precipitation suitability (C2, Negative)	Dimensionless	0.40	0.32
			Variation of sunshine suitability (C3, Negative)	Dimensionless	0.15	0.12
			Frost-free period change (C4)	Dimensionless	0.10	0.08
			Changes in disaster losses (C5)	Dimensionless	0.05	0.04
	Land use category B2	0.20	Soil and water loss rate (C6)	Percentage	0.30	0.06
			Reclamation Index (C7)	Percentage	0.70	0.14
Adaptability A2	Agricultural production category B3	0.67	Effective irrigation area ratio (C8)	Percentage	0.10	0.07
			Per capita area of arable land (C9, Negative)	hm2/People	0.35	0.23
			Per capita grain output (C10, Negative)	kg/People	0.25	0.17
			Yield per unit area (C11, Negative)	kg/hm2	0.15	0.10
			Mechanized agriculture (C12)	Wkw	0.10	0.07
			Amount of chemical fertilizer applied (C13)	t	0.05	0.03
	Socioeconomic category B4	0.33	Per capita income of farmers (C14)	Yuan/People	0.42	0.14
			Ratio of nonagricultural output to value (C15)	%	0.36	0.12
			GDP per capita (C16)	Yuan/People	0.22	0.07

, seven sensitivity indicators (including five climate sensitivity indicators and two land use indicators) were raster calculated based on GIS, and sensitivity evaluation results were obtained (Figure 5). The sensitivity index of major agricultural areas ranged from 0.62 to 0.81, with an average value of 0.72.

In terms of spatial distribution, in most of Ulanqabu except Siziwang Banner; Jining District; Shangdu County and Huade County; Harqin Banner, located in Chifeng City; Taiqi, located in the southwest of Xilin Gol League; Xilinhot City, located in the middle of Xilin Gol League; Helin County and Qingshuihe County, located in the south of Hohhot City; Wuchuan County, located in the northeast of Hohhot City; most of Baotou City; the northern part of Ordos City and Wuhai City; and southern Bayannur City, the sensitivity index of the region was higher than 0.75. The sensitivity of Baotou City and the areas to the west, along with the southern areas of Hohhot, Xilinhot and Kharqin Banner of Chifeng City, is higher because of the adverse change of climate suitability. At the same time, the variation of disaster damage is also greater in Ordos City and Bayannur City. In Ulanqab City, the major factors are the change in the amount of disaster damage and the heavy soil erosion in Xilin Gol League. The common influence of these factors is the main factor of high sensitivity.

5.2. Evaluation of Adaptability

5.2.1. Evaluation of the Adaptability of Agricultural Production

According to the agricultural production adaptability index (Figure 6) calculated by seven indicators, the areas with high agricultural production adaptability are in the central area of Ulanqabu City, the eastern area of Baotou City, the southwest area of Xilin Gol League and the northern area of Hinggan League. The effective irrigated area in these areas is small, while the per capita potato planting area and output are relatively high. However, the degree of mechanization is relatively backward, and the amount of fertilizer application is also at a low value, so the adaptability of agricultural production is at the lowest. On the contrary, most of the remaining agricultural areas, due to the high proportion of effective irrigation area, low per capita sowing area, high degree of agricultural mechanization and high fertilizer application, possess relatively high adaptability.

5.2.2. Evaluation of Socioeconomic Adaptability

Socioeconomic adaptability is mainly determined by local farmers’ income, nonagricultural production value and per capita GDP (Figure 7). The areas with good economic conditions are mainly in the city of Baotou, Ordos, Hohhot and central Xilingol League, while the rest of the areas are relatively poor, resulting in their lower adaptability.

5.2.3. Evaluation of Adaptability

The adaptability index of the main agricultural regions ranged from 0.61 to 0.90, with an average of 0.76. In these regions, the central part of the city of Tongliao, the central part of Hohhot, the southern part of the city of Bayannur and the northern part of the city of Ordos were the high adaptation areas. However, the south of Baotou, the majority of the Ulanqab zone, the southwestern Xilingol League, the northern Hinggan League and the central and eastern parts of the city of Hulunbuir had lower adaptability values (Figure 8).

5.3. Vulnerability Assessment

The sensitivity and adaptive capacity indices were calculated in the grid, and the results of the climate change vulnerability assessment were obtained. The Climate Change Vulnerability Index of the study area was 0.5~1.0, and the average value was 0.75, showing high vulnerability in the middle and low vulnerability in the east and west parts of the region. Vulnerability was highest in the central Ulanqab zone, southern Hohhot, southern Baotou and southwestern Xilingol League. The eastern regions of Hulunbuir, Hinggan League and Tongliao as well as southern Ordos were the least vulnerable, while most of the rest of Chifeng, Bayannur and northern Ordos were moderately vulnerable (Figure 9).

The degree of vulnerability of potato production to climate change can be reflected, to some extent, by the disaster rate in the potato cultivation area. However, it is difficult to obtain the degree of meteorological disaster-related losses in potato production, as disaster losses often reflect the combined losses of many crops, which are difficult to separate and calculate accurately. In this project, the yield losses caused by increases or decreases in the sown area and natural disasters are calculated by separating the climatic yield and the trend yield by using the actual yield and the sown area from previous research results [55]. According to a comparison of the average damage rate and the vulnerability assessment results of potatoes grown in Inner Mongolia over the past five years, the assessment results are basically consistent with the distribution of the damage rate in most areas of the main potato production areas. This serves as a preliminary verification of the rationality of the project evaluation results.

6. Discussion

(1)

In terms of the research to date, most studies focus on the entire growth period of agricultural ecosystems, with little attention given to the climate vulnerability of crops during each growth period. The different requirements for climate conditions and the temporal distribution of the climate during different growth stages of crops often lead to differences in climate vulnerability during each growth stage. Although the changes in climate suitability for potatoes at different growth stages have been examined in this study, when comprehensively assessing crop climate vulnerability, the entire growth stage was evaluated. In the future, we can continue to conduct separate evaluations at each growth stage.

(2)

The vulnerability assessment uses the comprehensive index analysis method, but the existing indicator system is not yet perfect, and construction of an indicator system should be improved in the future. Currently, researchers have mostly constructed indicator systems with two aspects (climate sensitivity and adaptability), but measures to mitigate climate change are gradually receiving attention from governments and scientists around the world. When evaluating agricultural climate vulnerability in the past and predicting short-term vulnerability, it is necessary to construct a more reasonable indicator system with respect to both the sensitivity and adaptability aspects. When predicting long-term agricultural climate vulnerability, it is necessary to consider the ability of society to mitigate climate change.

(3)

Due to the severe adverse effects of extreme climate events on agriculture, this study on vulnerability tentatively considers changes in the climate suitability for potatoes and uses its reverse effects to indirectly characterize the adverse effects of extreme climate conditions on potato production. This is a bold attempt to study the impacts of extreme climate events on agriculture, and further research, testing and validation of crop vulnerability impact assessments are needed in the future.

(4)

Expert scoring and the Analytic Hierarchy Process (AHP) are often used in determining the weight of indicator systems. Human factors are often involved, which can lead to deviations between research results and reality. To compensate for these shortcomings, artificial neural network models have been introduced and scientific achievements have been made, but there are shortcomings at both the theoretical and applicability levels. Therefore, how to scientifically and reasonably determine weights remains the focus of future exploration [52].

(5)

In the future, vulnerability impact assessments should be further tested and verified, and field investigation should be strengthened to accumulate a large number of first-hand observation data and fully supplement the comprehensive impact assessment of potato pests and diseases, such as late blight, powdery scab and black spot.

7. Conclusions

Based on the findings of this project, there are significant regional differences in the vulnerability of potato production to climate change in Inner Mongolia, with the central Ulanqab region being the highest vulnerability region and the rest of the eastern and western regions being low vulnerability regions. The high vulnerability of potatoes is mainly due to their high sensitivity to climate, inappropriate land use, poor ecological environment for agriculture and the low adaptability of farmers. For areas that are vulnerable due to high climate sensitivity, such as Baotou and the southern part of Hohhot, scientific response measures should be strengthened to adapt to current climate change trends, and agricultural water-saving irrigation technology and film mulching technology should be promoted to improve water use efficiency. Adjusting the sowing date of potatoes can help in avoiding high temperatures during the key period of yield formation, and strengthening field management to improve the utilization rate of light energy may be helpful. For areas that are vulnerable due to land use, such as Xilingol League and Ulanqab, the harsh natural environment, combined with man-made deforestation and land reclamation, has led to a sharp loss of vegetation, serious soil and water loss and farmland desertification. By moving away from the traditional, ineffective mode of production, adjusting the structure of agricultural production and energy consumption, strengthening vegetation restoration and preventing and controlling soil erosion, human disturbance to the natural environment and agricultural production can be reduced [1].

Some areas that are vulnerable due to low adaptability, such as central Ulanqab, southwestern Xilingol League, northern Hinggan League and eastern Baotou, are relatively underdeveloped in terms of economic development and the application of science and technology, and the farmers within these areas experience poor living standards and a lack of funds and technology to adapt to climate change. Therefore, in these regions, efforts should be made to speed up the pace of economic development and the construction of new villages; to raise the income level of farmers; and to encourage farmers to improve their crop varieties and apply advanced production technologies to improve their ability to adapt to climate change [1].

On the basis of previous studies, this study shifted the perspective of vulnerability research from agroecosystem to single crop, and developed from single yield factor to multi-factor. At the same time, the climate suitability of potato was innovatively applied as the key sensitivity index, thus replacing climate variability, so that the evaluation model and the crop’s demand for climate factors were more closely related, achieving a vulnerability-weighted comprehensive assessment. This is an innovation in studying the effects of climate change on a single crop.