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We obtained global land use and land cover (LULC) data from 1992 to 2015 from the European Space Agency (ESA), with a spatial resolution of 300 m. The latest reprocessing of five global satellite systems was obtained, including NOAA-AVHRR, ENVISAT Advanced Synthetic Aperture Radar (ASAR), SPOT vegetation, and the PROBA-V and MERIS Full Resolution (FR) and Reduced Resolution (RR). This dataset, which has been available online since April 2017, remains one of the most complex global land cover products because of its refined spatial resolution (300 m) and temporal availability (Chen et al., 2019). Efforts were made in this study to assess the consistency of the product with the noted products to minimize the uncertainty in the estimation of the change in the cropland area (Keys, 2005). We used the kappa coefficient, contingency matrix, user error, producer accuracy, overall accuracy, and building procedures to reduce the macro errors to evaluate the statistical accuracy (Wang et al., 2021). Finally, through comparison with other effective validation products, we evaluated the time consistency of the data (See et al., 2015).
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The latest global LULC classification divides surface objects into seven categories: cropland, forestland, grassland, wetland, urban construction land, unused land and water bodies. Cropland refers to land where crops are planted, including cropland, newly developed, reclaimed and arranged land, and leisure land (such as rotation land and rotation land). The land is planted mainly with crops (including vegetables), with sporadic fruit trees, mulberry trees, or other trees. Cultivated beaches and sea beaches that can ensure a harvest for one season on average every year also have been considered to be cropland (Portmann et al., 2010).
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We extracted data for the global grain yield and the grain yields of 30 countries with large cropland areas from 1992 to 2015 from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) (Tscharntke et al., 2012) to facilitate international comparison and data collection. We selected 14 types of grain as the research object, including wheat, rice, maize, sorghum, barley, buckwheat, rye, fonio, oats, canary seed, millet, quinoa, triticale, cereals, and nes (Agnolucci et al., 2020). We analyzed the relationship between the grain yield and the cropland area from 1992 to 2015 by taking the total grain yield, the grain harvest area, and the grain yield per unit area as the research objects.
$$ {Y}_{i}=\frac{{F}_{i}}{{C}_{i}} $$ (1) where Yi is the yield of year i, Fi is the crop yield acquired from the FAO of year i, and Ci is the cropland area estimated from the European Space Agency’s Land Cover Classification System (ESA-LCCS) dataset of year i.
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Based on the ESA data, we calculated the cropland area in each year from 1992 to 2015. The Manner-Kendall (M-K) non-parametric test method has been used to study historical and future evolution trends in precipitation, temperature, the gross domestic product (GDP), population, and other factors around the world. In this study, we used the M-K method to calculate the turning point year of the change in the cropland area. The formulas used are as follows:
$$ {{S}} = \sum\limits_{{{q = 1}}}^{{{m}} - {{1}}} {\sum\limits_{{{p}} = {{q + 1}}}^{{m}} {{{{\rm{sgn}}}}} } \left( {{{{X}}_{{p}}} - {{{X}}_{{q}}}} \right)$$ (2) $$ {\rm{sgn}}\left( {{{{x}}_{{p}}}{{ - }}{{{x}}_{{q}}}} \right){{ = }}\left\{ {\begin{array}{*{20}{l}} {{{ + 1}}}&{\left( {{{{X}}_{{p}}}{{ - }}{{{X}}_{{q}}}} \right) > {{0}}}\\ {{0}}&{\left( {{{{X}}_{{p}}}{{ - }}{{{X}}_{{q}}}} \right){{ = 0}}}\\ {{{ - 1}}}&{\left( {{{{X}}_{{p}}}{{ - }}{{{X}}_{{q}}}} \right) < {{0}}} \end{array}} \right\}$$ (3) where S is a normal distribution with a mean of 0 and represents the test statistics, and m is the number of samples. For different values of q (i.e., p ≤ m, q ≠ p), the distributions of Xp and Xq are different. When the absolute value is greater than 1.28, 1.64, and 2.32, they pass the significance test at the 90%, 95%, and 99% confidence levels, respectively. The trends with P ≤ 0.1 are statistically significant in this study (Luo et al., 2022).
The year 2004 was the turning point of the change in the global cropland area. We calculated the annual average rate of change of the cropland area (R) before and after 2004 and 2012. The same method was also used in China, India, and the United States:
$$ R = \frac{{C}_{i}-{C}_{j}}{i-j} $$ (4) where R is the rate of change of the global or country’s cropland area, i and j represent the year series, and Ci and Cj represent the cropland area in years i and j, respectively.
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We extracted the cropland area data for 229 countries around the world from remote sensing images and selected the 30 countries with the largest average cropland areas during the study period. These countries dominated the trends of the global cropland area and grain yield. We used histograms to show the grain yields and harvested areas in the 30 major food-producing countries on the map of the global cropland area distribution to better describe the response of the grain yield to the changes in the global cropland area and the cropland intensification (Wu et al., 2020).
We calculated the correlation coefficients to quantitatively describe the response of the grain yield to the changes in the cropland area and harvested area to determine their contributions to the global grain yield (Wu et al., 2020). The correlation was calculated as follows:
$$ Q = \dfrac{{\displaystyle\sum\limits_{i = 1}^n {{M_i}{t_i} - \frac{1}{n}\displaystyle\sum\limits_{i = 1}^n {{M_i}\displaystyle\sum\limits_{i = 1}^n {{t_i}} } } }}{{\displaystyle\sum\limits_{i = 1}^n {{t_i}^2 - \dfrac{1}{n}{{\left( {\displaystyle\sum\limits_{i = 1}^n {{t_i}} } \right)}^2}} }}$$ (5) where Q is the expected value of the linear trend; and n is the study period from 1992 to 2015. Mi is the independent variable corresponding in year i. If the correlation coefficient of the regression equation passed the significance test at the 0.05 and 0.01 confidence levels (P < 0.05 and P < 0.01), the small probability event occurred, and Mi decreased or increased to significant and highly significant levels, respectively.
$$ \;R^{2} = \frac{{{\rm{Cov}}{{(x,y)}^2}}}{{{\rm{Var}}(x) \times {\rm{Var}}(y)}} $$ (6) where Cov is the covariance and Var is the variance; R2 ranges from 0 to 1; x is the change in the cropland area or harvested area; and y is the grain yield between 1992 and 2015. Focusing on the results of the calculations, if the value of R2 is closer to 1, the correlation between the cropland area or the harvested area and the grain yield is stronger.
A New Indicator for Global Food Security Assessment: Harvested Area Rather Than Cropland Area
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Abstract: Cropland area has long been used as a key indicator of food security. However, grain yield is not solely controlled by the area of the cropland. Therefore, we proposed a new indicator to assess food security. Results show that from 1992 to 2004, the global cropland area increased by 840 200 km2 (99.4%), but the grain yield increased only by 310 million t (29.1%); and from 2004 to 2015, the cropland area decreased by 39 000 km2 (4.64%), but the grain yield increased by 370 million t (70.84%). This result showed that grain yield was not linearly correlated with cropland area, and delimiting the threshold of cropland protection may not guarantee food security. Combined with further correlation analysis, we found that the increase in the global grain yield was more closely related to the harvested area (R2 = 0.94), which indicated that the harvested area is a more scientific and accurate indicator than cropland area in terms of guaranteeing food security. Therefore, if governments want to ensure the food security, they should choose a new and more accurate indicator: harvested area rather than cropland area.
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Key words:
- global change /
- food security /
- harvested area /
- cropland area /
- grain yield
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Figure 10. Fitting analysis chart of grain yield, cropland and harvest area in China, the United States and India. Slope < 0 (> 0), indicating that the two independent variables are negatively (positively) correlated, the width of red shadow represents the value of R2 and the correlation between independent variable
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