Impacts of climate change on suitability zonation for potato cultivation in Jilin Province, Northeast China

Study area

This study was conducted in Jilin Province, which is located in the center of Northeast China (40°52ʹ N–46°18ʹ N, 121°38′ E–131°19ʹ E) and covers an area of approximately 187,400 km², with an elevation varying from 5 m to 2,691 m (Fig. 1). The study area has a temperate continental monsoon climate and is climatologically humid, semi-humid, and semi-arid from the southeast to the northwest. The annual mean temperature and annual total precipitation form a southeast-northwest gradient; the eastern part is relatively humid and rainy while the western region is dry in the summer months. Generally, 70–80% of the annual precipitation occurs from June to September, with the most abundant rainfall in the east. The long-term average annual temperature and average annual rainfall are 5.8 °C and 687.0 mm, respectively⁴⁹. Crop cultivation is mostly concentrated in the black soil region⁵⁰. The soil types of cultivated lands mainly include black soil, sand, and paddy soil, which are suitable for potato growth.

Potato growth is highly dependent on temperature and light. Jilin Province, as one of the main potato-producing areas in China, possesses sufficient sunlight and exhibits large temperature difference between day and night. Generally, potato cultivation occurs from April to May, depending on the lowest temperature (5 °C), and potatoes are harvested from August to October of the same year. Among potato production areas, mid-late maturing cultivars (e.g., Yanshu No. 4, Atlantic, Jishu No. 1, and Summer) account for about 70%, while early maturing cultivars (e.g., Favorita, Youjin, and Fujin) account for 30%⁵¹.

Data

Climate data

Climate data were obtained from the National Meteorological Information Center, China Meteorological Administration (http://data.cma.cn), including 51 national standard meteorological stations in Jilin Province (Fig. 1). The meteorological data contain daily average temperature, daily maximum temperature, daily minimum temperature, daily sunshine hours, and daily precipitation during 1957–2018. Based on the periods of potato sowing and harvesting in Jilin Province, the climate data between April 1 and September 31 each year were selected. To avoid the impact of extreme weather within a single year on the inter-annual climate change, we used 5-year moving average values of climate data rather than single-year values to establish a geo-climate model using regression analysis and evaluated changes in suitable areas for potato cultivation under the influence of climate change.

Topography data

Topography data were extracted from the digital elevation model (DEM) sourced from the geospatial data cloud SRTM (http://www.gscloud.cn). Through a series of processes such as adding X–Y axis, splicing, vector data layering, filtering, cropping, and resampling of raster data on the ArcGIS platform, digitized elevation model (90-m resolution) maps were used to derivate layers such as longitude, latitude, slope, and aspect (Fig. 1).

Soil data

Soil mechanical composition data (81 sampling sites) were extracted from the National Science and Technology Infrastructure Platform (http://soil.geodata.cn) and soil physico-chemical property data (79 sampling sites) were provided by the Soil and Fertilizer General Station of Jilin Province (http://www.jltf.cn). The sequence number of the occurrence layer is 1, and the thickness is about 20-50 cm. The soil properties extracted included contents of soil sand, silt, and clay, pH, and contents of nutrients such as organic matter (OM), quick-acting potassium (QAK), available nitrogen (AN), and available phosphorus (AP) (Fig. 1; Tables S1-S2).

The soil data were rasterized using kriging. First, the soil mechanical composition data were converted into spherical coordinates, and then ordinary Kriging interpolation was used to spatialize the soil mechanical composition data. co-kriging was used to interpolate spatialize the soil physico-chemical property data. Due to limited soil samples and the lack of a continuous dataset in the study area, the soil data in 2018 were selected as a fixed background for the analysis.

Analysis of climatic factors

We used six climatic factors in this study. Usually, potatoes have has different requirements for light, heat, and water in each growth and development stage. We used average daily temperature during the growth period (ADT/°C, mean of daily average temperature from April 1st to September 30th) and active accumulated temperature ≥ 10 °C (AAT/°C d, sum of active accumulated temperature ≥ 10 °C from April 1st to September 30th) from 1961 to 2018 to reflect the temperature conditions of potato growth^{52,53,54,55,56}. ADT at 14–17 °C was evaluated as “Most suitable”; 10–14 °C or 17–20 °C as “Suitable”; 8–10 °C or 20–24 °C as “Sub-suitable”; < 17 °C or > 24 °C as “Not suitable” for potato growth in the study area. AAT for mid-late maturing varieties at 2000–3000 °Cd was evaluated as “Most suitable”; 1,500–2,000 °Cd or 3,000–6,000 °Cd as “Suitable”; 1,300–1,500 °Cd or 6,000–8,000 °Cd as “Sub-suitable”; < 1,300 °Cd or > 8,000 °Cd as “Not suitable”.

The average temperature in July (ATJ, mean of daily average temperature in July) and the day/night temperature difference from July to August (DIF/°C, mean of the day/night temperature difference from July 1st to August 31st) are the key climatic factors for the expansion of potato chunks, which have significant correlation with the meteorological yield of potato 53–57. ATJ at 16–20 °C was evaluated as “Most suitable”; 15–16 °C or 20–24 °C as “Suitable”; 12–15 °C or 24–28 °C as “Sub-suitable”; < 10 °C or > 28 °C as “Not suitable”. DIF at 8–12 °C was evaluated as “Most suitable”; 5–8 °C as “Suitable”; 2–5 °C as “Sub-suitable”; < 2 °C as “Not suitable” in the study area.

During the growth and development of potato, there is a great demand for water, especially from the budding stage to the swelling stage of potato growth, which are extremely sensitive to water supply^{52,53,54,55,56}. The total precipitation during the growth period (PP/mm, sum of the daily precipitation from April 1st to September 30th) at 700–900 mm was evaluated as “Most suitable”; 600–700 mm or 900–1,200 mm as “Suitable”; 500–600 mm or 1,200–1,500 mm as “Sub-suitable”; < 500 mm or > 1,500 mm as “‘Not suitable” in the study area.

Short daylight and appropriate high temperature during the seedling stage are beneficial to promote potato root development, forming strong seedlings and increasing potato formation^{52,53,54,55,56}. The total sunshine duration during potato growth (SD/hours, sum of the daily sunshine duration from April 1st to September 30th) at 900–1,200 h was evaluated as “Most suitable”; 700–900 h or 1,200–1,500 h as “Suitable”; 400–700 h or 1,500–1,800 h as “Sub-suitable”; < 400 h or >1,800 h as “Not suitable”.

Methods

First, climatic factors were simulated using geo-climate models. Then, the AHP-PCA model was employed for suitability evaluation, and the satellite-based gridded environmental data were applied for suitability mapping. Finally, the degree of changes in climatic factors and suitable geographic ranges were calculated. These data were interpolated into the surface grid data with a spatial resolution of 0.03° × 0.03° (~3 km × 3 km)^57,58. All maps and statistical analyses were generated using ArcGIS 10.4.1⁵⁹ and R 3.6.3⁶⁰.

Geo-climate model building

Topographic factors such as longitude, latitude, and altitude dominate the distribution of climate factors, and directly affect the solar radiation budget and atmospheric circulation, which makes the climate resources to demonstrate obvious spatial differences in both vertical and horizontal directions^61,62. Based on the meteorological data and geographic information of each meteorological station, we established geo-climate models and used them to calculate the climate distribution of the study area. The difference between the highest temperature and the lowest temperature from July 1st to August 31st was used to calculate the grid layer of DIF. The relationship between climate zoning indicators and geographic factors is expressed as follows:

where, F is the simulated value of grid point of the climate zoning index; λ, φ, and h represent longitude (°), latitude (°), and altitude (m), respectively; f (λ,φ,h) is called climatological equation of regionalization index; and ε is the influence of local small topography and random factors on climate (i.e., comprehensive geographical residual term).

Residual correction : Affected by local topography and random factors, the variation of climatic factors is random, which will cause errors in the calculation of geo-climate models. Therefore, the inverse distance weight (IDW) routine in ArcGIS was used to derive the simulated value of the comprehensive geographical residual term ε raster⁶³. The interpolation calculation formula is:

where, ε is the simulated value of the grid point of the residual term of climatic factors; (n) is the number of meteorological stations; ({varepsilon }_{i}) is the residual value of the climate factor of the (i)-th meteorological station; ({d}_{i}) is the Euclidean distance between the grid point and the (i)-th meteorological station; k is the power of the distance.

AHP-PCA and GIS based suitability analysis for potato cultivation

The suitability map for potato cultivation was generated based on identified criteria that are relevant to the climatic, soil environmental, and geophysical conditions considered. Details of the data analysis procedure, model application, and suitability classification are described as follows.

• AHP-PCA model

  Analytical Hierarchy Process (AHP) is a multi-criterion decision-based approach developed for analyzing complex decisions involving multiple criteria^38,64,65. Principal Component Analysis (PCA) is a multivariate statistical data analysis technique that combines all input variables using a linear combination into a number of principal components that retain the most variance within the original data to identify possible patterns or clusters between objects and variables. In this study, we used AHP to calculate the weight of each zoning indicator in the evaluation index system^66,67, and then, we explored the comprehensive relationship of suitability evaluation factors using the grid calculator and PCA tool on the ArcGIS platform. The first principal component will have the greatest variance, the second will show the second most variance not described by the first, and so forth. In most cases, the first three or four raster bands of the resulting multiband raster from principal components tool will describe more than 95% of the variance, that is, the cumulative contribution rate of the principal component reaches more than 95%. The variance of the weighted original data becomes larger, leading to more scientific and reasonable evaluation results. In summary, the proposed approach is achieved as follows (Fig. 2):

  Step 1: The weight of each index was calculated by using AHP and consistency test;

  Step 2: The indicators were standardized using the Z-Score method;

  Step 3: The weights calculated in Step 1 were loaded onto the standardized indicators;

  Step 4: A standardized matrix was built and the correlation coefficient matrix was calculated;

  Step 5: The principal components was filtered and determined;

  Step 6: The score for each principal component was calculated;

  Step 7: A comprehensive score for all indicators was obtained.

• Establishment of indicator system and calculation of weight

  The assessment of climate change impacting suitability of potato cultivation has multiple objectives and levels. This paper combined comprehensive and hierarchical principles, relevant literature reviews^38,39,40,68, expert opinions, and characteristics of potato cultivation in Jilin Province to establish an index system for evaluation of ecological environment impact, including 18 evaluation indicators: ADT ( °C d), AAT ( °C), PP (mm), SD (h), ATJ ( °C), DIF ( °C), elevation (m), slope (°), aspect (°), hill shade, sand (%), silt (%), clay (%), OM (g/kg), pH, QAK (mg/kg), AN (mg/kg), and AP (mg/kg). These indicators were classified into three categories: climatic conditions, soil environments, and topography (Table 1).

  The weight of each evaluation indicator was determined by AHP. According to relevant literatures and expert opinions, we established a judgment matrix for these evaluation indicators. Pairwise comparison was used for obtaining the relative importance score between different indicators. The consistency of pairwise importance scales is one of the important measurements for successful decision-making by AHP, which could be checked using consistency ratio (CR). If CR < 0.10, the degree of consistency is satisfactory, whereas, CR > 0.10 indicates an inconsistency^63,69 (Table 1).

• Classification and mapping for suitability of potato cultivation

The natural breakpoint method in ArcGIS was employed to classify lands of the study area in terms of cultivation suitability. The study area was delineated into 4 zones: zone 1 (Not suitable), zone 2 (Sub-suitable), zone 3 (Suitable), and zone 4 (Most suitable) (Table 2).

After normalizing all indicators, the cultivation suitability index was established as follows:

where I is the suitability index for comprehensive evaluation, ({W}_{i}) is the weight of the indicator, ({X}_{i}) is the value after dimensionless treatment of the indicator, i is the comprehensive evaluation value of topography, climatic conditions, and soil environments. The larger the topography value was converted into a negative value for the calculation as the greater its value, the higher its negative impact on cultivation suitability. Meanwhile, the greater the pH value is, the more unfavorable the comprehensive evaluation of soil will be; the pH value was therefore inversed for the calculation.

Trends and fluctuations in changes of climatic factors and suitable areas

The fluctuations of various climatic factors over the past 58 years were analyzed by coefficient of variation (CV), which was calculated as CV = (standard deviation/mean) × 100%. Temporal trends in changes of climatic factors and suitable areas were calculated using ordinary least squares linear regression on annual data from 1961 to 2018. Among them, the trend in suitable area changes was calculated based on each grid. The significance of trends was estimated following a method that considers the temporal autocorrelation by reducing the effective sample size of the time series⁷⁰. And the significance of temporal trends was tested at P < 0.1⁷¹.

Source: Ecology - nature.com