**1. Introduction**

Recently, diet shifts have threatened global food security [1]. As the Food and Agriculture Organization of the United Nations reported, nearly 12% of the worldwide population faced severe food insecurity in 2020. The percentage is likely to rise in the coming decades [2]. Hence, implementing suitable agricultural adaptation to diet shifts is challenging for ensuring food security [3]. According to this aim, an essential measure of this adaptation is to reach a food balance between supply and demand [4]. The rationalization of the crop planting structure (CPS) contributes to optimizing the crop yield proportion to achieve this balance [5].

The CPS rationalization refers to the appropriate adaptation of crop composition and spatial distribution for agricultural development [6]. This adaptation is different from one country to another. For example, the main crops in the United States are soybean and maize [7]. Additionally, agriculture in Brazil and China have main crops of soybean and maize, respectively [8,9]. This situation showed that these two crops represented an

**Citation:** Li, Q.; Liu, W.; Du, G.; Faye, B.; Wang, H.; Li, Y.; Wang, L.; Qu, S. Spatiotemporal Evolution of Crop Planting Structure in the Black Soil Region of Northeast China: A Case Study in Hailun County. *Land* **2022**, *11*, 785. https://doi.org/10.3390/ land11060785

Academic Editors: Yongsheng Wang, Qi Wen, Dazhuan Ge and Bangbang Zhang

Received: 20 April 2022 Accepted: 24 May 2022 Published: 26 May 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

essential share of international trade. The interdependence of food trade intercountry ensures global food security [10]. However, irregular CPS evolution has been a significant obstacle to national food security, notably in China [11]. Previous studies show that this obstacle is mainly manifested in the irrational proportion of food yield [12]. Specifically, wheat, rice, and maize self-sufficiency have reached 95%, while soybean faces production shortages in China [13]. Consequently, the Chinese agricultural principal contradiction has shifted from an insufficient total output to a structural contradiction [14]. This situation results from China's market economy and the interference of the natural environment [15]. Hence, the Chinese government has undertaken policymaking to address this contradiction nationwide [16,17]. Geographic information about the spatiotemporal changes of the CPS is an essential basis for such policymaking [9]. Therefore, the study of CPS plays a strategic role in optimizing CPS and safeguarding national food security.

Previous studies on CPS have focused on two main aspects. The first was to analyze the interactions between CPS and other elements. The second extracts information on crops' spatial distribution and suggests the optimization of CPS. The study of interactions between CPS and other components involves several disciplines, including climatology [18], hydrology [19], ecology [20], and geography. The scholars have conducted research primarily in geography. They focused on the interaction between CPS with latitude, population density, and geographical location [21,22]. In extracting crop-distribution information and CPS optimization, scholars have mainly explored the spatiotemporal changes of CPS on national [23,24] and regional scales [25,26]. In China, researchers have conducted studies on different scales, such as the entirety of China [9], North China [27], Sanjiang Plain [5], Hunan Province [28], etc. These studies have promoted the optimization of China's CPS to safeguard national food security. However, research on the black soil region of Northeast China (BSRNC), which is an essential commercial grain production base, is lacking. In addition, most of these studies focused on characterizing the CPS for the entire study area through multiple counties [29]. They concerned a large region, and few studies investigated the geographic characteristics of the CPS within a small geographical entity such as Hailun County. For this reason, we seek to understand the aspects of CPS in BSRNC on a small scale.

Small-scale acquisition of CPS features specialized methods and data. Surveys, statistics, and remote-sensing image interpretation are the three primary methods for obtaining CPS information. Survey data are accurate, but obtaining CPS information for long time series is challenging [30]. Statistical data are available for accessing long time series of crop information. Restrictedly, statistics fail to reflect spatial heterogeneity [31]. With the advancement of remote-sensing technology, acquiring high-resolution, long-term series of remote-sensing images is possible [32]. Remote-sensing image interpretation provides rapid access to small-scale spatial crop information with long time series [33]. Furthermore, high-, medium-, and low-spatial-resolution images are employed for remote-sensing image interpretation. High-spatial-resolution remote-sensing images, such as SPOT, enable the accurate extraction of crop information. However, image interpretation based on such data requires a long access period and a large workload due to the low temporal resolution [34,35]. Low-spatial-resolution remote-sensing images such as MODIS provide a broader coverage area and higher temporal resolution. Nevertheless, it is difficult to guarantee the accuracy of extraction results [36,37]. Medium-spatial-resolution remote-sensing images, such as Landsat, enable the rapid and accurate acquisition of crop information [38,39]. Overall, remote-sensing interpretation at medium spatial resolution is preferred to obtain CPS of BSRNC on a small scale.

The BSRNC covers an area of 1.09 million square kilometers and contains 264 counties [40]. The BSRNC is a significant supplier of soybean, maize, and rice in China and contributes a quarter of the national food yield [41]. Nevertheless, the irrational crop yield proportion has hindered agricultural development in this region. This hindrance is shown by a significant decline in soybean yield and increased maize and rice yield [42]. In addition to the unit yield, the crop yield changes are mainly due to the CPS adjustment [43]. From

this fact, the CPS evolutionary study contributes to a new round of CPS policymaking in BSRNC, thus optimizing the food yield proportion. Furthermore, the adjustment policy of large-scale CPS needs to be practiced in small regions. Therefore, this study selected Hailun County as an example and aimed to summarize the geographical characteristics of CPS spatiotemporal dynamics from 2000 to 2020. Specifically, the objectives of this study are: (1) to analyze the temporal dynamics of crop area, (2) to analyze the spatial dynamics of crop distribution, and (3) to seek to determine CPS type and analyze CPS distribution characteristics. These findings can geographically inform county-level CPS adjustment in BSRNC to ensure regional food security.

#### **2. Materials and Methods**

#### *2.1. Study Area*

Hailun County is located between latitudes of 46◦58 –47◦52 N and longitudes of 126◦14 –127◦45 E, in the central part of BSRNC [44]. The regional landform is characterized by southwestern plains and northeastern hilly, with an average elevation of 239 m. The northeastern most hilly area is mainly covered with forests. Hailun County has a humid continental climate, with an average annual temperature of 2.48 ◦C. The average yearly precipitation is 550 mm/year. The main rivers and reservoirs distributed in the territory are Tongkeng River, Zhayin River, Hailun River, Dongfanghong Reservoir, Lianfeng Reservoir, etc., (Figure 1).

**Figure 1.** Study area.

Hailun County is a nationally renowned grain-producing county. The cultivated land area is about 310,000 hectares, accounting for 63.3% of the total land area. The principal crops are soybean, maize, and rice. In 2020, Hailun County's food yield reached 132,500 tons, and its agricultural income was 65% of the county's gross domestic product, which indicates that agriculture is the leading industry [45].

Black soil is the primary soil type in Hailun County, accounting for 63.4% of the total land area. This soil features excellent permeability and water retention, as well as great fertility potential, which provides favorable conditions for crop growth. To conduct observations and studies on black soil agriculture, the Chinese Academy of Sciences has established research stations in Hailun County [46]. In addition, the Chinese government has implemented relevant policies (Guidance on the Structural Adjustment of Maize in the "Sickle Bend" Area, etc.,) to adjust Hailun County's maize area. To optimize the planting structure of soybean and maize, Hailun County was also listed as a pilot area of the national crop rotation fallow system in 2017 [11]. Therefore, Hailun County is a typical area for studying the CPS of BSRNC.

## *2.2. Data Resources*

The crop phenology features one crop per annum in the study area. At the transplanting and tillering stages, the rice showed significant spectral differences in the remotesensing images from May to June. Hence, we chose these images for identifying rice. From mid-late July to mid-August, soybean is in the stage of podding to maturity, and the plants begin to turn yellow. Meanwhile, maize is in the milky ripeness stage, and the plant greenness is still high. Therefore, we chose images from this period to distinguish between soybean and maize [47].

We collected Landsat4-5 TM, Landsat7 ETM+, and Landsat8 OLI remote-sensing images with a spatial resolution of 30 m for five years (2000, 2005, 2010, 2015, and 2020) (Table 1). In addition, we collected the administrative division vector data and DEM data of Hailun County. These data were sourced from the Geospatial Data Cloud Platform (https://www.gscloud.cn/home, accessed on 15 May 2020).


**Table 1.** Details of satellite imageries.
