1. Introduction
No other activity can match agriculture in importance. In most of the underdeveloped world, agriculture is not only the main employment but also a proportion of national income and export earnings [
1]. Between 1990 and 2005, cropland is estimated to have increased at a slower rate than population growth, and at the same time, increases in yields and decreases in cropland occurred infrequently on a global scale [
2]. Over the past two decades, cropland around the world has expanded dramatically [
3], mainly due to increased demand for food from a growing population [
4]. Recent studies predict that approximately 500 million hectares of additional cropland will be needed by 2050 to meet global food demand [
5], which will increase the pressure on natural habitats [
6]. Considering also that the United Nations’ Sustainable Development Goals (SDGs) for 2030 call for balancing the increase in agricultural production with the maintenance of ecosystem services [
7], it is crucial to have precise data on the current availability of agricultural land to make accurate food forecasts and ensure food security and sovereignty. This will empower us to develop effective economic, political, and governmental strategies in collaboration with the relevant authorities to achieve optimal territorial zoning.
We now have digital tools such as remote sensing, which has long been recognized as an effective means of mapping large-scale land cover [
8]. In recent years, coarse spatial resolution global cropland maps have been used that cannot adequately capture cropland dynamics (loss or gain) (e.g., <1 km
2 or 100 ha) [
9]. This is problematic, for example, for conservation strategies. Since most of the significant human impacts on the earth cannot be captured at the scale of the Google Earth Engine platform [
10] and must be done in adequate time frames for efficient management, we recognize that the GEE platform is a tool that facilitates the acquisition and processing of images that allow us to evaluate the large amount of information obtained by satellites with greater precision and in less time [
11].
GEE has a vast, free, petabyte-scale catalog of satellite imagery and geospatial datasets. It is the only one that brings together information from Landsat, Sentinel, MODIS satellites and data on climate models, temperature, geophysical features, and other satellite imagery from different international agencies to standardize scales and generate time series [
12]. It is very intuitive and allows the input of local data and the export of information for further processing or visualization within its geographic information system (GIS) software, such as QGIS (Version 3.28) and ArcGIS Pro (Version 3.1.2) [
11]. In Peru, only agricultural research on monocultures, such as rice cultivation, has been conducted using the GEE platform [
13]. The platform was used for the first time in Peru to provide information on rural agriculture. This will help to manage resources, make decisions, monitor crops, and update the crop calendar. Also, the transition of crops from natural area to cultivation, or from forest to cultivation, or from natural area to protected area can be managed, as well as the desert zones in Peru that are generally Agri Export Zones and their implications. In this sense, remote sensing data help rural agricultural development agencies fill information gaps [
14]. Smallholder farmers are the most important custodians of plant genetic resources for in situ conservation [
15]. However, rural agriculture in Peru is complex; it involves poverty, development, food systems, climate change, and agricultural production management [
16]. For example, global factors such as the Severe Acute Respiratory Syndrome (SARS-CoV-2) pandemic that initiated in Peru a policy of quarantine and social isolation imposed by the declaration of a National Public Health Emergency as of March 2020 have already generally affected small-scale agriculture, which provides about 70% of the country’s food, at all stages of the food production chain [
17].
In Peru, the restrictions to curb the pandemic primarily affected low-income families in poorer areas of Lima and the main cities. Faced with the situation, they had to migrate back to their centers of origin in the hope of finding better conditions for their families [
18]. This is called reverse migration, which can have both positive and negative effects on different ecosystems. Despite efforts in South America, high informal employment and social inequalities undermined the effectiveness of these countries’ responses [
19]. The effects of this reverse migration on the dynamics of areas dedicated to agriculture, with a focus on food security and sovereignty, have not been well addressed by empirical studies.
The concern for the above led to the presentation of this research, whose objectives were to conduct a multitemporal analysis of land use and land cover change (LULC) using the Google Earth Engine platform in nine rural districts mainly dedicated to agriculture, using eight types of land cover, representative of Peru. Then, trend values were estimated for changes in agricultural areas until 2027 to estimate possible trends in a post-pandemic scenario. The analyses were made using as a reference base the changes in farming areas for the years 2017–2022 in these rural districts of Peru, dedicated to agriculture, and emphasis was placed on the dynamics of change from 2020, in which the pandemic emergency began in Peru.
Our findings span a national scope because we studied nine rural districts representative of the three main regions of Peru: coast, highlands, and jungle, covering the north, center, and south. Given that future farmland abandonment and its effects on environmental services require explicit policy action [
2], we expect these data to inform and strengthen policy proposals.
3. Results
3.1. Land Use and Land Cover Change (LULC) on the Coast
The dynamics of agricultural coverage in the Paccho district (
Table 3,
Figure 3) showed an increase in 2022 despite the volatility of its values during the years evaluated. In 2017, 13.10 km
2 of agricultural area was recorded, and by 2022, it was 20.48 km
2 of agricultural area.
In the rural district of Guadalupe (
Figure 3,
Table 4), there was a slight increase in agricultural areas for 2022 to 80.9226 km
2, compared with 80.208 km
2 in 2017, and the largest extensions of coverage were presented in agricultural areas for all years. Artificial areas, water bodies, burned areas, and forest plantations showed a decrease over the years. Meanwhile, forests and bare soil increased until 2022.
In the Coast (South) region where Atico is located, land use dynamics denoted an increase in agricultural areas, ranging from 0.7434 km
2 in 2017 to 1.3095 km
2 in 2022 (
Figure 3,
Table 5). The largest areas were recorded in bare soil in all years. Artificial areas and forests showed an increase in 2022, while forest areas decreased.
Except for the district of Atico (located in southern Peru), in the coastal districts of Paccho and Guadalupe, as an effect of the pandemic (years 2021 and 2022), a decrease in the area with agricultural and forest cover was observed, increasing bare soil.
3.2. Land Use and Land Cover Change (LULC) in the Sierra
In Sapillica, agricultural cover increased and decreased each year to such an extent that it shrunk from 83.3355 km
2 in 2017 to 67.2849 km
2 in 2022. Other coverages that eventually decreased in this zone were artificial areas, herbaceous coverages, and bare soil. The cover types that increased their areas in 2022 compared with 2017 are forests, water bodies, burned areas, and forest plantations (
Figure 4,
Table 6).
On the contrary, in Lonya Chico, agricultural areas increased progressively over the years (16.3917 km
2 in 2017 to 24.7311 km
2 in 2022). In addition, the cover of artificial areas, forests, herbaceous cover, and bare soil increased over the years. Meanwhile, the coverage of water bodies decreased, burned areas decreased to a lesser extent, and forest plantations decreased considerably (
Figure 4,
Table 7). This district in the highlands is the only one that in 2022 will occupy the first place in extension occupied by agricultural cover.
In the rural district of Lucanas (
Figure 4,
Table 8), the agricultural cover showed a progressive and considerable decrease in agricultural cover, ranging from 51.8706 km
2 in 2017 to 19.8567 km
2 in 2022. The coverages of burned areas, forests, and bare soil also showed a decrease in their areas, while water bodies and herbaceous cover increased.
In general terms, in the rural areas of the highlands region (
Figure 4; and
Table 6,
Table 7 and
Table 8), agricultural land is more dispersed throughout the district. Lucanas (south) and Sapillica (north) showed less agricultural activity compared to a district such as Lonyachico (northeast) where, despite their small size, small-scale agriculture is basically for self-consumption. Overall, agricultural areas increased.
Changes in land use in the rural districts of the highlands of Peru were very different. Soils with herbaceous cover decreased since 2021, increasing the area with forest cover in Sapillica (Sierra Norte), while in Lonyachico it appeared that forest areas were converted to agricultural land. In the Lucanas district, there was an increase in the area with herbaceous cover in 2022, which could explain the decrease in the extension of crops.
3.3. Land Use and Land Cover Change (LULC) in Selva
For the rural district of Santa Rosa (
Figure 5 and
Table 9), the agricultural coverages denoted an increase during the last 5 years, except in 2021 (pandemic year). However, by 2022, an agricultural area of 116,307 km
2 was shown, making this value the largest area concerning the rest of the coverage. Other areas that finally grew were the coverages of artificial areas, burned areas, herbaceous coverages, and bare soil. The areas that decreased in these years were forests and water bodies. Inversely in Jepelacio, the dynamics of land use show that agricultural areas decreased to 152.3862 km
2 in 2022, even though 161.9703 km
2 were recorded in 2017 (
Figure 5,
Table 10). A significant reduction of artificial land cover areas was shown until 2022, while forest areas, burned areas, and water bodies increased. In Villa Rica, where there are mainly export monocultures, the dynamics of agricultural cover over the years was unstable, decreasing from 230.3874 km
2 in 2017 to 138.8745 km
2 in 2022. Artificial areas, forests, water covers, herbaceous covers, forest plantations, and bare soil finally increased until 2022; and bare soil decreased its agricultural area (
Figure 5,
Table 11).
Regarding the dynamics of change in agricultural areas in the rural districts in the jungle region (
Figure 5 and
Table 9,
Table 10 and
Table 11), there were changes in increase and decrease indistinctly. It should be noted that agriculture did not lead as the largest area in any of the districts (Santa Rosa, Jepelacio, and Villa Rica), moving into second place to forests.
Conversely, the three studied jungle districts seemed to have large extensions of land for agriculture associated with crops that effectively require large extensions, such as coffee, cocoa, rice, etc. In addition, the configuration of the cover types is very similar, indicating greater homogeneity according to the type of land use.
In the rural districts of the Peruvian jungle, changes in use appeared to be between forest areas with herbaceous cover and space dedicated to crops.
3.4. Trends in Agricultural Area Forecasts in Time Series for Rural Districts of Peru
Figure 6 shows the changes in the amount of land dedicated to agriculture in the nine rural districts of Peru. In the Coastal region, an increasing dynamic of agricultural area forecasts was observed in time series in Atico and Paccho, including the year of the beginning of the COVID-19 pandemic that affected the entire world. Although Atico presented a progressive increase in areas in all the years evaluated, Paccho increased the forecasts in agricultural areas between the years 2017 and 2021, and by the year 2022, it presented a considerable reduction in agricultural areas. The trends in Guadalupe were the opposite; this district presented a decrease in agricultural areas starting in 2020 and a single year of increase in agricultural areas (2018). The trends for the year 2027 differed between districts on the coast. Guadalupe and Paccho showed a decreasing trend in agricultural areas that, if the trend continues, without changing factors, could disappear in Paccho or be drastically reduced in Guadalupe. Atico showed an increasing trend until 2027.
The dynamics of agricultural area forecasts in the highlands of Peru were different in each district. While in Lonya Chico the trends finally increased until 2022, despite the reduction of agricultural areas in 2021, in Lucanas these areas showed a trend of annual reduction during the 6 years of evaluation. The rural district of Sapillica presented a dynamic of annual sequential increase and decrease (years 2017–2022). Despite the dynamics of change in varied agricultural areas that were presented in the three districts, the trends of agricultural areas for the year 2027 showed an increase in the forecasts in the three zones; these values are represented under the growth model curve in
Figure 6.
In the Peruvian Selva region, the dynamics of agricultural area forecasts in the years 2017–2022 were varied and not sequential in each year and for each district evaluated. Despite this, it was noted that between the pandemic years 2017 to 2021, agricultural forecasts increased in all three zones. Santa Rosa was the district where agricultural area forecasts increased (2017–2022). Meanwhile, the districts of Jepelacio and, to a greater extent, Villa Rica showed that agricultural areas decreased during the years evaluated.
In the synopsis for Peru, the dynamics of change in agricultural areas evaluated (year 2017–2022) were different according to each region and according to each district. Analyzing the agricultural area in the zones, from 2017 to 2022, it increased in all rural districts evaluated in the Coast region (Paccho, Guadalupe, and Atico) and decreased in all rural districts evaluated in the Jungle region (Villa Rica, Jepelacio, Sapillica, and Lucanas). Meanwhile, in the Sierra region, the increase in agricultural areas occurred in Lonya Chico, and the decrease in Lucanas and Sapillica. Likewise, for the annual trends to 2027, only in the Sierra region was there a trend of increasing agricultural area forecasts in the three zones, together with Atico in the Costa region and Santa Rosa in the Selva region. Meanwhile, Guadalupe and Paccho in the coastal region and the jungle region, Jepelacio, and Villa Rica showed decreasing trends in agricultural forecasts for 2027.
5. Conclusions
The Google Earth Engine platform made it possible to quantify areas of different land cover types (LULC) in nine representative rural districts dedicated to agriculture in Peru (years 2017–2022). In all regions of Peru, all cover types proposed in the satellites were found (artificial areas (AA); agriculture (A); forest (B); water bodies (CA); burned areas (ZQ); forest plantations (PF); herbaceous (H); and bare soil (SD)), although not all districts have all types of land cover. The dynamics in the increase and reduction of areas are indistinct for the three natural regions, so it is deduced that social economic or environmental factors influence in a specific way these dynamics of cover. Especially in times of pandemic, the change in agricultural area between 2020 and 2021 presented the same trend of increasing areas in the Coastal region, decreasing areas in the Sierra region, and an indistinct trend of areas in the Selva region. Only in the Sierra was there a trend of increasing agricultural area forecasts in the three zones, along with Atico on the Coast and Santa Rosa in the Jungle. Guadalupe and Paccho on the coast and in the jungle, Jepelacio, and Villa Rica showed decreasing trends in agricultural forecasts.
This work demonstrates that the migratory phenomena caused by the measures imposed by the government to contain the COVID-19 pandemic provoked a change in the extension of land dedicated to agriculture in Peru. Cultivated land on the coast was reduced and increased in the districts of the highlands. However, more factors need to be incorporated to obtain more accurate projections.