1. Introduction
In the past few decades, climate change has turned into one of the most studied topics, with its serious impact on socio-economic, environmental, and biological issues, by numerous domestic and foreign scholars [
1,
2,
3]. Notably, agriculture in developing countries is likely one of the sectors most negatively affected by climate change [
4,
5]. Despite significant advances in technology and crop yields, food production and safety remain deeply dependent on weather and climate change, as temperature and precipitation are the main drivers of crop growth [
6,
7,
8].
As a result, food security problems caused by extreme climate events have sparked research and public interest in the analysis of climate change [
9,
10] and agricultural production [
11]. A number of studies have explored the effect of climate change on crop yield [
12]. As mentioned above, long-term fluctuations in crop yield are closely related to climatic factors, such as temperature and precipitation [
13,
14]. Nevertheless, most of these studies focused on single timescales using the traditional time series analysis (correlation analysis and linear regression models) or crop growth models [
13,
15,
16,
17]. The effect of climate variables on crop yields is scale-dependent [
18,
19,
20]. Single timescale analysis cannot comprehensively reveal the response of crop yield to climate change, and may ignore some important information on other timescales. Meanwhile, climate variability is traditionally very well characterized across timescales, such as annual, inter-annual, and inter-decadal scales [
21,
22,
23,
24,
25].
In addition to climate change, human activities are a major driver of crop yields [
26,
27,
28,
29]. Over the past two decades, crop yields have increased dramatically, driven by the use of fertilizers, improved crop varieties, and agronomic management [
30,
31]. For example, Niu et al. [
32] indicated that at the present cultivation levels (planting of 67,500 plants/ha with 225 kg/ha nitrogen application), the genetic improvement, agronomic-management improvement, and genotype–agronomic management interaction had resulted in yield increases in Northeast China during the last six decades. During this time, contributions leading to increases were 45.4%, 30.9%, and 23.7%, respectively. However, how crops respond to climate change and human activities on different timescales remains unresolved. In fact, the time series of crop yield and climate variables containing different frequency components, such as long- and short-term oscillations, are generally non-stationary [
33,
34,
35,
36]. To increase our awareness of the impacts of climate change and develop adaptation practices, it is necessary to separate the effect of climate change from the effect of each climate variable on observed changes in crop yield [
37]. Therefore, selecting an appropriate method to divide those non-stationary time series into variations on multiple timescales tends to be critical, which may be conducive to truthfully and comprehensively reveal the relationship between crop yield and climate change at different timescales.
The ensemble empirical mode decomposition (EEMD) method used to linearize and smooth the nonlinear and non-stationary signals [
38] has a significant advantage in dealing with non-stationary signals. The method can separate the fluctuations of different timescales and produce a series of intrinsic mode functions (IMFs) containing local characteristic information on different timescales of the original signal and residual (R), which retains the data in the process of decomposition characterization [
38]. In this study, we employ the EEMD method to explore the multi-scale characteristics of crop yield fluctuations and their correlations between major influencing factors, which provide new insight into the impact of various climatic influencing factors on maize yield fluctuations at multiple timescales. This work helps in quantifying the impact of climate change on grain production variations, thereby providing support for reliable grain production forecasting. Maize is the crop with the largest planting area and production in China. In accordance with the FAO, the planted area of maize in 2016 was 36.8 million ha and production was 219.6 million tons [
39]. The main crop in Heilongjiang Province is maize, which is also an important commodity grain production base in China. Consequently, this study selected maize yield in Heilongjiang Province as the research object. Moreover, since the overall trend in enhancing food production is predominantly caused by human activities (e.g., technological advances, fertilization, irrigation, etc.) [
40,
41], the impact of human activities on food production was also investigated in this study.
In this study, maize yield, climate change, and human activities at multiple timescales were extracted using the EEMD method. Their effect at multiple timescales was further explored to provide scientific knowledge for food security. Therefore, the main objectives of this study are: (1) To determine which timescale is dominantly responsible for maize yield; (2) to evaluate the relative importance of climate change and human activities on maize yield.
Section 2 describes the study database and methods.
4. Discussion
Possible causes of the dominant effect of climate and human activities on maize yield were explored at different timescales. As shown in this study, the DVVD and Sen-slope methods were used to analyze the effect of climate change and human activities on maize yield at 3.1-year timescale, which showed that climate change had a significant impact on maize yield (
Figure 7b). In accordance with the results in
Table 2, the change cycle of the IMF1 of climatic factors is basically consistent with the IMF1 of maize yield, and the variance contribution rate of the IMF1 of these climatic factors accounts for more than 50% of the total variance contribution rate. Therefore, climate change mainly affects the 3.1-year timescale oscillation in maize yield. Moreover, previous studies considered the relationship between maize yield and climate change (temperature and precipitation) in Heilongjiang Province, China [
13,
60].
For the 18.5-year timescale and the long-term trend, human activities were the dominant factor. On the one hand, it can be found in
Table 3 that the long-term increasing trend was the main component for EIA and CFF, which indicated that the effect of human activities on maize yield was mainly reflected in the trend [
36,
61]. On the other hand, agricultural policies in Heilongjiang Province have a significant impact on maize yield [
38,
62,
63,
64,
65]. During the period 1979–1994, the implementation of the household contract responsibility system resulted in a significant increase in maize production [
64,
65]. There was a clear downward trend of maize yield in 1995–2007, for which the main reason was that from the early stage of reform and opening up to the first half of the 1990s, the increase in grain production brought by the increase in agricultural production enthusiasm led to the “difficulty in selling grain” of farmers. The problem of increasing production and not increasing the income of farmers became increasingly serious, which seriously affected the enthusiasm of farmers to engage in agricultural production [
38,
62]. In addition, it was found that there was a significant increasing trend in maize yield during the period 2008–2015, which was mainly due to the fact that under the influence of the central policy of benefiting farmers and the revitalization of the old industrial base in northeast China, the agricultural production efficiency in Heilongjiang Province had been greatly improved [
63]. Moreover, the change cycles of food policies are basically the same as the IMF3 of maize yield (at 18.5-year timescale). Food policies mainly work on maize yield through measures, such as fertilizer application and irrigation improvement by farmers.
5. Conclusions
In this study, multiple timescale analysis for the relationship between maize yield and climate change and human activities was performed employing the EEMD method. Maize yield in Heilongjiang Province can be divided into 3.1-, 7.4-, 18.5-, and 37-year timescale oscillations and a long-term trend during the period 1979–2015. Maize yield dominated by 3.1- and 18.5-year timescale oscillations and the long-term trend with high variance contributions. The DVVD and Sen-slope methods with multiple timescale analysis showed that human activities predominated the original sequence of maize yield series. As the timescale increased, there were different effects of climate change and human activities on maize yield, in which climate change mainly affected maize yield at short timescales, and human activities mainly influenced the original sequence, 18.5-year oscillation, and long-term trend of maize yield. As a whole, human activities had a stronger impact on maize yield than climate change at the 18.5-year timescale and long-term trend, but the effect of climate change on short-term fluctuations in maize yield was stronger than human activities. The findings of this study indicated that the effect of human activities on the original sequence of maize yields has obscured the effect of climate change. Therefore, it is beneficial to carry out multiple timescale studies to help better characterize the impact of human activity and climate change on maize yields, which will improve maize yield forecasting and modelling. This study facilitates a better understanding of the relationship between crop yield and climate change and human activities at multiple timescales. Furthermore, it provides few scientific references for food security under global climate change.