A system dynamics approach to food security: The case of Turkey

Predicting a sustainable food safety policy for the near future is among Turkey's priority problems. In this context, this study aims to predict Turkey's sustainable food safety policies. For this reason, the system dynamics model, which is a dynamic cycle-based method with stock and flow diagrams, is used in this paper. This study supposed the six different scenarios for 2020 and 2050. Data were selected as population, productivity rate, arable land fertility rate, and annual food consumption (per capita). The purpose of creating these scenarios; To determine the most appropriate policy to ensure food safety in Turkey. In the first scenario, we assumed that the current situation continues. In the second scenario, the average productivity rate was increased by 1.5%. The third scenario assumes that annual per capita food consumption rises to 1.2 tonnes per year. In the fourth scenario, the total fertility rate is accelerated by 2%. In the fifth scenario, we assumed that the arable land loss rate decreased by 1/3. Finally, we assumed that the sixth scenario covers all the second, third, fourth, and fifth scenarios and that 2 points reduce food losses. In conclusion, the findings show that food security responds positively in scenarios 2 and 6. However, in other scenarios, food security is negatively affected. The findings show that the sixth scenario is the best-case scenario. To ensure food security, it is necessary to reduce arable land losses and food waste. Training farmers and control of the food supply chain will be beneficial for sustainable food security in Turkey. We recommend that policymakers consider these recommendations.

The temporal and spatial scales of arable land loss and its impact in Guangxi, China: A Commentary

Cropland abandonment because of rural depopulation or policy interventions has become a key issue in Chinese mountainous areas. One such region is the Guangxi Karst Mountainous Area (GKMA), a zone where more than 59% of total land area is hilly and arable land of a commercially viable size is almost non-existent. The rugged terrain and land fragmentation in upland karst areas result in the scarcity of land suitable for cultivation. Although depopulation and declining agriculture since 2000 within the GKMA have led to vast areas of abandoned cropland, the spatiotemporal distribution that underlies this pattern as well as its causes remain little understood. Geomorphic features also bring about differences in the distribution of settlements. Settlements with different degrees of distribution are accompanied by spatial differences in cultivated land resources, which lead to differences in the sufficiency of cultivated land resources. In this paper we provide an overview of the magnitude of the problem of arable land loss. settlements and analyze the spatial distribution and the spatial agglomeration of the cultivated land.

Perception-based Assessment of Ecosystem Services of Abijata-Shalla National Park Wetlands in the Central Rift Valley of Ethiopia

Abijata-Shalla Park was established as one of Ethiopia's national parks to safeguard wetlands and ecosystem services (ESs). Some of the ESs that are offered by the wetlands are currently depleting and disappearing rather than being protected. Understanding the drivers behind these changes can help individuals and policymakers design mitigation measures. The objective of this case was to assess ESs and the drivers of change with highlighting on the Abijata wetland. In addition to a household survey and group discussion, personal interviews and field observation were employed to collect data. Using these data, the various ESs were assessed and ranked from 1-10 according to local perception. Grading scales such as very high (−2), high (−1), neutral (0), low (+1), and very low (+2) were employed to analyse the drivers of ESs change. Analyses of the study revealed that some of the ESs, including fish, papyrus, water reeds, hunting and spiritual services, existed before 1991, but have since disappeared from the site. Twenty ESs are available; 11 services pertain to provisioning, followed by 4 regulating, 3 cultural and 2 supporting services. Wetland for cultivation ranked highest, followed by domestic water supply and pasture. All services, with the exception of arable land and pasture, are on the decline. Water abstraction is the primary driver of ESs change, followed by population growth and deforestation. The park existed as a "paper park." Water withdrawals from the Ziway-Shalla sub-basin should be restricted. Instead, focus on water conservation strategies to make better use of abstracted water.

Comparison of Winter Wheat Extraction Methods Based on Different Time Series of Vegetation Indices in the Northeastern Margin of the Qinghai–Tibet Plateau: A Case Study of Minhe, China

The northeastern margin of the Qinghai–Tibet Plateau (QTP) is an agricultural protection area in China’s new development plan, and the primary region of winter wheat growth within QTP. Winter wheat monitoring is critical for understanding grain self-sufficiency, climate change, and sustainable socioeconomic and ecological development in the region. However, due to the complex terrain and high altitude of the region, with discontinuous arable land and the relatively low level of agricultural development, there are no effective localization methodologies for extracting and monitoring the detailed planting distribution information of winter wheat. In this study, Sentinel-2A/B data from 2019 to 2020, obtained through the Google Earth Engine platform, were used to build time series reference curves of vegetation indices in Minhe. Planting distribution information of winter wheat was extracted based on the phenology time-weighted dynamic time warping (PT-DTW) method, and the effects of different vegetation indices’ time series and their corresponding threshold parameters were compared. The results showed that: (1) the three vegetation indices—normalized difference vegetation index (NDVI), normalized differential phenology index (NDPI), and normalized difference greenness index (NDGI)—maintained high mapping potential; (2) under the optimal threshold, >88% accuracy of index identification for winter wheat extraction was achieved; (3) due to improved extraction accuracy and resulting boundary range, NDPI and its corresponding optimal parameter (T = 0.05) performed the best. The process and results of this study have certain reference value for the study of winter wheat planting information change and the formulation of dynamic monitoring schemes in agricultural areas of QTP.

Natural Afforestation on Abandoned Agricultural Lands during Post-Soviet Period: A Comparative Landsat Data Analysis of Bordering Regions in Russia and Belarus

Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method’s details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985–2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus’ district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.

Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

Data-Driven Forest Cover Change and Its Driving Factors Analysis in Africa

Forest cover plays an important role in sustaining ecological security to realize Sustainable Development Goals (SDGs). The research target area is composed of the African region which is experiencing unprecedented deforestation based on the data collection from 54 countries and regions between 2000 and 2020. Spatial autocorrelation analysis, global principal component analysis, and geographic detector model have been used as the core research tool. The temporal and spatial patterns of forest cover change in Africa and the driving effects of population growth, economic and trade, social development, arable land expansion, and other factors on forest cover change in different periods have been demonstrated. The findings are as follows: 1) extremely unequal distribution of Africa forest has caused forest area reduction in 20 years. The reduction quantity of forest has been illustrated from strong to weak: Central Africa (strongest), East Africa (higher strong), West Africa (medium), South Africa (higher weak), and North Africa (weakest). However, the forest reduction area in West Africa with the original ratio is the most significant. More than 80% of the forest area reduction in Africa has occurred in 14 countries, just five national forest areas to achieve the net growth, but the increase amount was only 1% of loss amount. 2) The spatial pattern of forest cover change in Africa contracted and clustered gradually, especially after 2012. Algeria was the hotspot cluster of Morocco and Tunisia, forming the increase area of forest cover in North Africa. Zambia, the coldest point, gathers Angola significantly, while the Democratic Republic of the Congo and Tanzania form a significantly reduced forest cover area. 3) Total population, land area, cultivated land, urban population, consumer price index, and birth rate are the main factors influencing the temporal evolution of forest cover change in Africa. It can be divided into four stages to interpret the different explanations and significance of each factor for forest cover change in the study area.