The Assessment of Sub-Saharan Africa's GHG emission from cropland in comparison to some developing nations, its environmental economic impacts, and mitigation measures

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Satellite image of sentinel-7 was deployed to capture real-time virtual images of land use land cover (LULC) showing a proportion (58%) of massive agricultural land in the region still lies uncultivated due to its losses to climate catastrophe that endangers and rendered between 0–40% usefulness for food production valueless. This study deploys various measuring metrics to examine the intensity of climate variability using panel data, as well as real-time data from remote sensing (RS) to verify and make a comparison of CO 2 eqKg emitting capacity from leading croplands major countries in SSA. Estimate stochastic frontier analysis (SFA) was used to compute and assemble from 1988 to 2022. The result revealed within the six closely monitored countries their emitting rate with South Africa led as the highest emitter of CO 2 equivalent in kg in these years, with its peak annually recorded in 1990 at an estimated value of 64kg CO2eqKg followed by 2017 with a value of about 58 kg CO 2 eqKg while Ethiopia came second with its second-highest emitting rate in 2007 with a value of 24kg CO 2 eqKg followed by Nigeria with mean contributory value of 21Kg CO 2 eqKg. It unveiled an estimated total cropland of 10881657.5 square hectares in North central Tier 2 (Nigeria, Niger) and North central Tier 3 (Sudan, Ethiopia) as the hotspot of the GHG emission index. The study further presented 2013–2022 as the most diminution years in the region with a forecasted 21% ecological resources (aquatic species) decline in the coming year with a burden of more disastrous ecological resources in most likely affected nations such as Nigeria, South Africa, Kenya, Mali, and Burundi, Zambia as they are mapped as the most vulnerable to these unforeseen longtime environmental consequences. The study suggests adopting locally developed innovative technologies compatible with current climate resilience strategies, to be implemented through a comprehensive approach. GHGs anthropogenesis climate change remote sensing climate-smart technologies LULC Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction There are urgent risen global concerns about the unabated climate crisis in the Sub-Sahara Africa (SSA) countries caused by anthropogenic activities, and the scenario tends to cripple the natural resources availability with eroded soil, thereby undermining natural-based resources efficiency (Alewell et al. 2019 ; Mc Carthy et al. 2018), Unfortunately, there exist insignificant scientific response measures to curtail this now, hence the situation led to an intensification of food insecurity across SSA and the rest of the World, as SSA is considered a major food producer in the continent. The enormous food wastage (deterioration, loss) has risen annually as the climate continues to pose global concerns that question the survival of human existence in the region, and the adverse effect on food security has become inevitable since the decades ground effort so far in curtailing ravaging diminishing natural resource seems (Brenya et al. 2024 ). Successive shocks from the COVID-19 pandemic have worsened the food scarcity in the region. According to the Global International Monetary Fund (IMF), this backlog has significantly increased by not more than 30 percent since early 2020 (Mitra et al. 2022 ). The implication is that about 12% of the entire SSA population in 2022 suffered severe malnutrition, hunger, and deaths in some critical situations. The SSA over the years has continued to witness climatic catastrophic events that trigger unexpected drought, cyclones and floods occurrence, high Land Surface Temperature (LST), and rivers submerging, all these dilapidating situations hasten biodiversity losses, setting back UN SDG 1 and 2 targets in the region (Muza 2024 ; Quartey et al. 2024 ). These come with no surprise as studies have shown such an unprepared major climate event, people die of hunger and the survivors are less productive, already achieved response measures are thwarted, thereby upsetting the systemized operational economic activity, destabilizes ecological management order as seen recently even in fast-developing countries like China were little can be done at this critical point, except a resilient and quick measure can salvage such situation (Wu and Wang 2024 ). Efforts towards assessing the impact of climate in the SSA ecological niche, need to be understood as a broadened issue that encompasses ecocentrism, biocentrism, and anthropogenic activities (Fig. 1 ). The latter has significant impacts as it influences the formal (ecocentrism and biocentrism) while interfering with the niche building. Moreover, succession processes trigger excessive GHG emitting activities in a process called "niche construction" Without prejudice to the existing climate-distorting effects, it is believed that man can salvage this menace by drafting ecologically friendly norms, enforcing them to regulate the interplays of the 3 drivers above which shape the likelihood of detoxification of co-contamination of organic pollutants as well heavy metallic (soil nutrients) leaching into the surroundings. This will be a key pointer to the need for natural environment restoration and resilience (Oña et al. 2021 ; Wang et al. 2022 ). Previous studies have proven that these factors impact not just the ecosystem but lives, altering life expectancy, opportunities, and health status of habitat, reflecting the quality of life thereof (Igwe et al. 2023 ). Tacking climate and its variability over the years has been successively achieved in some developed and developing countries (USA, Germany, Russia, India, and China) which SSA countries can replicate based on their regional climate and relief adaptability. Remote Sensing (RS) and Geographical Information Systems (GIS) have been reported to be game-changers in controlling and regulating activities of crops and the field, help provide real-time information that guides food decision-makers, environmentalists, monitor anthropogenic activities and determining appropriate responses to climate change while keeping to sustainable intensification of agricultural operation to proffer solution ineffective climate policy (FOA 2023 ). A similar study conducted in SSA countries estimated the land surface temperature (LST) to be alarming (Sajib and Wang 2020 ). The continuous man disruption and its process are illustrated (Fig. 1 ). LST is the measuring metric to determine the thermodynamic skin cropland by measuring the infrared radiation coming from the surface, its specialized method to examine urban heat islands, monitored crop growth and for detection of forest fires (Eckmann et al. 2008 ; Zhou et al. 2011 ). Moreover, it can also be used for related studies like hydrological processes with the application of the principle Normalized Difference Water Index (NDWI) which is useful in assessing the intensity of climate on cropland through its LST (Crow and Wood 2003 ). Again, regulation of environmental limiting factors from GHG emission as well as its interaction with inhibitor plants hormone, gibberellin and xylem which controls evapotranspiration rate to enhance crop maturity rate (Aslan and Koc-San 2016 ; Crow and Wood 2003 ; Kustas and Anderson 2009 ; Shwetha and Kumar 2015 ). It's important to highlight here basic cropland parameters (air temperature, relative humidity, soil temperature, activities of microflora and microfauna) which reflect the activity rate of soil microorganisms is crucial for a better understanding of the indexing rate of GHG from an agricultural perspective, and better propose sustainable responses to tackle it in the SSA as well as at the global level. The assessment of the current state of food production in this region (Fig. 1 ) shows the series of ecological breakdowns caused by unsustainable direct and indirect agricultural activities which hastens soil degradation, either erosion, salinization, or bogging due to poor management practices (Andersen 2024). Sequential stages of it led to unabated GHG emissions with its reaction bringing more disbalance in the ecosystem, derailing its restoration potential, reduced essential minerals that support crop production. In extreme soil temperature situations, it can escalate the evapotranspiration rate thereby causing low soil moisture, crop wilt and deterioration of perishable produce in the absence of storage facilities. Based on the UN Food and Agriculture Organization's (FAO) recent findings (FOA 2023 a), SSA suffers annual 38 kg ha − 1 (i.e., 26 kg N, 3 kg P, 9 kg K nutrient depletion with a heavy burden on soil infertility. As demonstrated in Figure (2), climate change takes credit for tremendous damages done not just to food security but also to the overall environment outlook, and human health. According to the World Health Organization (WHO), the state of the surroundings contributes up to 18–20% of the populace's health. This makes it impossible to achieve speedy healthy population growth in the absence of food security as food nutrient deficiency in the daily diet of humans gradually exposes them to certain diseases such as kwashiorkor, fever, low mental development, and high mortality rate at the same time exposes the crop to some seasonal pest's infestation. In addition, crop growth and development depend mainly on soil nutrient availability and the percent of moisture. Even though most of these nutrients can be contaminated with metallic ions according to my previous studies this situation emanates from unsustainable agricultural activities in Africa (Igwe et al. 2023 a). Notable research proves frequent heavy rainfall can harm crops by eroding the soil surface causing essential plant nutrient leaching (Gowda et al. 2018 ). The continuity of these situations can lead to soil runoff (agrochemicals) into oceans thereby contributing heavily to freshwater water contamination with heavy metallic ions (Igwe et al. 2023 b). In Fig. 2 the study highlighted the trends of climate and its variability and demonstrated how it can spark GHGs emission triggers and impact food insecurity anywhere in the world, with the outcome of acute hunger and severe poverty. The challenge of adopting smart agriculture technologies in the region has aggregated the climatic risk impact on household farmers' depicting their incapacities to invest in agriculture food innovation, value addition, integrated farming, and ecological services to support organic farming, hence they keep recycling old systems of agriculture, promoting further climate variability in the region (Wakweya 2023). Consequently, an increased food insecurity could jeopardize the hard-earned improvements in the incomes, education, and health status within the African continent. Recent studies show that Peri-urban food insecurity and coping strategies remain an understudied topic in SSA (Haile Aboye et al. 2024 ). These and other serious humanitarian and economic implications could spark conflict and large-scale migration, which calls for urgent actions to save the rampaging loss in biodiversity due to the climate change emanating from excessive emitting of GHG. Further claims show that one-third of the global droughts occur in SSA, this makes the region the most vulnerable part of the world that is prone to food insecurity, coupled with limited technologies, and a high index poverty ratio which summed up with complicated and weakening efforts to reduce GHGs ineffective (World Bank Group 2023 ). Another scenario is the continued removal of forests in addition to the already experienced desert encroachment in the northern part of SSA like Niger and Chad makes the situation more grievous, causing the source of livelihood more difficult to access. The condition has been triggered on SSA cropland with long ages loading of the lithosphere, atmosphere, and hydrosphere with excessive GHGs such as nitrous oxide (N2O), methane (CH4) spillage, and other destructive chemical from agricultural runoff into the cropland which enhances GHG build-up resulting in 265 times the global warming potential of carbon dioxide (CO2) per mass over a 100-year time horizon (Uen and Rodríguez 2023 ). The consequential impact of anthropogenesis drives the negative effect of today's environmental and food crisis as shown. (Fig. 2 ) In another context, the climate crisis has the propensity to make conditions better or worse for growing crops in different regions, especially in water risk-prone areas according to RS with aided GIS surveillance as presented a schematic erupting process and stages of human-induced climatic change (Fig. 2 ). Recent study shows slight changes in temperature, rainfall, combined with frost-free days conditions tends to prolong crop growing seasons (Gowda et al. 2018 ). Although a longer growing season has two coins, both positive and negative impacts on the food production cycle, as some farmers have working capital to wait for their yields for the plant longer-maturing crops (annual crops), while others may not, resulting in more irrigation over a longer, this can lead to high cost of production. Moreover, the situation may warrant long-term greenhouse gas emission potential which can endanger the surface air, damage crops, plants, forests, and loss of important species of biodiversity (Akon et al. 2011 ). However, there are some scientists with a different view on the approach to tackle this discourse, they argue that there is no proof to address the climate challenge to regulate the GHG emission index since the causes are multi-complicity and interconnected (McCarthy et al. 2018 ), hence in their view it almost impossible to lay hand on a single approach to tackle the impact of climate on the nature. Currently, ground breakthrough findings by renowned climate scientists on climate in the pile the line (Hansen et al. 2023) support an integrated approach to tackling climate challenges. The rich agricultural land in Sub-Saharan Africa demands responsible stewardship to promote soil fertility, ecosystem balance, and biodiversity conservation, ensuring long-term food security for local farming communities. It seems infeasible due to the climate emergency couple's poor responses and a low technology upheaval to sustainably revolutionize food production, (Kihara et al. 2020), hence this study considers it as one of the critical issues to examine climate vulnerability in instigating high GHGs and proffer possible remedy. The novelty of this research is the determination of the proportional cropland with a high GHG index in SSA countries secondly to equate the cropland land and its productiveness as influence my climate changes and its variability effects on the economic status of the populace. Afterward, it provides grassroots local involvement and foreign response driven by synergetic intervention based on the ecological and geographical compatibility of the soil in the study area. This will hopefully bring the long-term desire for sustainable agricultural transformation and corresponding quick ecological restoration to mitigate seasonal excessive GHG emissions sparking climate change during the crop production cycle. This is essential in the region as research on the topic is limited. This paper is organized as follows. "Approach and Method" This section describes the method and process of how data were collected and analyzed. Material and Methods The study deploys an application of remote sensing (RS) Sentinel-7 near real-time (NRT) data to assess the land surface temperature (LST) effects on normalized difference vegetation index (NDVI) as well as normalized difference water index NDWI from within the study area. Panel data were used to statistically analyze the Global Warming Index (GWI) in contrast with SSA, and the impact factor on dominant crops in the regions (southern, central, and eastern) coast of African countries that made up SSA nation within 1988–2022. The empirical measurement of the emission index based on panel data was determined in CO2eq/kg, while the crop losses were weighed in kilograms per year. The effect of the losses was evaluated by the number of agricultural-related job losses in the study period. The study deploys spatial production allocation model layer access to the data generated from RS and GIS due to the peculiar fragmentation nature of cropland existing in SSA countries' farmlands. Hence the approach involves strategic steps (mapping 2 agrarians in 2 states each from the southern, central, and eastern parts of SSA), and evaluating crop losses from climate variability. To validate the information, collect and process the study employed the services of expert knowledge from agronomists and the International Institute of Tropical Agriculture (IITA), and the global cropland emission index was conducted from data of global cropland monitoring tool (v1)(Tools 2024 ) relevant literature on existing climate responses with similar case studies was used to proffer remedies based on the study findings. These provide the study easy access to real-time and secondary data used to identify the dominant crop cultivated area and support the study justification on the discrepancy from yield losses per year due to excessive soil temperature and evapotranspiration. The entire vegetation in the SSA study region was categorized with pixels (Fig. 3 b) which were grouped into four categories and were assigned a group under sparse vegetation, shrubland, barren lands, forest, and grasslands (savannah vegetation) following NDVI pixel classification range (Nordborg et al. 2018 ; Usťak et al. 2019 ), and quantified abandoned cropland by comparing individual pixels of the ESA CCI-LC and C3S-CDS datasets for all years between years of study. Study area Sub-Saharan Africa (SSA) is the African continent's southernmost region, made up of forty-seven (47) out of 54 African countries which include; Nigeria, Cameroon, Namibia, South Africa, Mali, Senegal, Ghana, Gambia, Rwanda, Zambia, Uganda, Zimbabwe, Sierra Leone, Niger, United Republic of Tanzania, Togo, Swaziland, Seychelles, Mozambique, Madagascar, Sao Tome and Principe, Guinea-Bissau, Kenya, Somalia, Mauritius, Lesotho, Equatorial Guinea Eritrea, Benin, Democratic Republic of the Congo, Botswana, Burundi, Chad, Liberia, Burkina Faso, Comoros, Malawi, Gabon, Côte d'Ivoire, Central African Republic and Cape Verde among others (UNEP-UN Environmental program 2023) with exclusion of Algeria, Egypt, Morocco, Somalia, Sudan, and Tunisia. The continent is the second largest in the world after Asia and it is unique and distinct both geographically and ethno-culturally. It is known for its massive cropland which serves as the main source of economy and foreign exchange to date. Almost the entire SSA is in the tropics and displays tropical and subtropical climatological zone characteristics. (Fig. ) Figure. 3 represents about 70% of the landmass of the continents of SSA that lie between the tropics of Cancer and Capricorn respectively positioning the region to experience a tropical climate. These geographical positions instigated the highly vulnerable rate of food production in the region (Misiou and Koutsoumanis 2022 ), and this has negatively compelled farmers to overreliance on rain-fed agricultural production. Notably, the adverse effects of the above have been reported in recent studies to susceptible vagaries of weather and climate, continuous low outputs, and fluctuations in agricultural market shares and food commodities and also threaten natural lifecycle (Elias 2021 ; Wheeler and Von Braun 2013 ). GHG Emission Computation Description The study adopted two systematic computation processes (i) stochastics frontier analysis (SFA) to examine panel data from 1988 to 2020 and (ii) one EX-ACT to estimate the GHG balance at the level of each region over the simulated 32-year period (Fig. 4 ) in one scenario (optimistic AP adoption) with simulation each afterward comparison was conducted among the global emitter while the economic impact was measured in terms of yields loss per Kg like per Kg GHGs emission index. EX-ACT boasts advanced tools for estimating GHG emissions over extended periods, offering a distinct advantage over Tropical farms' one-year assessment timeframe. This calculation was developed by a UN Food and Agriculture Organization (FOA) expert since then this GHG calculator has been in use (Bernoux et al. 2010 ) for similar cases in computing CO2 and its equivalent gas emissions. Meanwhile, data were assembled from a panel dataset consisting of a cross-sectional and time-series dataset compared with RS and GIS Satellite data generated from the land surface temperature (LST) air temperature, precipitation, and relative humidity. The time frame spans from 1988 to 2020. This is followed by the description of the study area which unveiled geographical features such as relief, climate, and types of vegetation understudied. To effectively monitor the emission index and its effects on crop yield the study uses remote sensing and geographical information system (AFROCOVER) and Sentinel-7 and 3 to access data for the period under investigation. Afterward, the study is presented in the "Results and Discussion" section discusses the obtained findings of this work. Finally, conclusions are drawn in the "Conclusion" section. Temperature Algorithm The reference weather stations for the key production areas of these crops were extracted from the Global Yield Gap Atlas (GYGA), with climatic drive changes recorded using a precise ecological arithmetic model built on secondary data from the World Metrological Organization (WMO). The growing degree days (GDD) values were calculated as shown in Table 1 $$\:\begin{array}{c}GDD=\sum\:_{\varvec{i}=1}^{365}\varvec{M}\varvec{a}\varvec{x}\:\left(0,{\varvec{T}}_{\varvec{i}}-{\varvec{T}}_{\varvec{b}}\right)\#\left(1\right)\end{array}$$ Table 1 Result obtained for Regional GDD and GYGA-CZ Value Region GDD (°Cd) GYGA-CZ Value South 0–3669 1000 South Central Tier 1 3670–4164 2000 South Central Tier 2 4165–4790 3000 Southeast 4791–5830 4000 North Central Tier 1 5831–6949 5000 North Central Tier 2 6950–7211 6000 North Central Tier 3 7212–8964 7000 East 8965–9511 8000 West 9512–13250 9000 North > 13250 10000 Note: These are approximate prevalence values subject to calibrating measuring device(s) The average density of air and land surface temperature (Table 1 ) was computed using the GDD model (Eq. 1) in which T i is the temperature (°C) for each time step and is T b the base temperature (0°C for our calculations) was used for the computation according to (Licker et al. 2010 ) Sub-Saharan African Quotient of water $$\:\begin{array}{c}AI\:=\:\frac{\varvec{M}\varvec{A}\varvec{P}}{\varvec{M}\varvec{A}\varvec{E}}\:\#\left(2\right)\end{array}$$ Table 2 Result obtained for LST and corresponding GYGA-CZ Value Surface temperature AI (-) Mean seasonal Precipitation Range GYGA-CZ Value south 0–3694 000 South Central Tier 1 3695–4894 100 South Central Tier 2 4895–5790 200 Eastern 5791–6690 300 North Central Tier 1 6691–7587 400 North Central Tier 2 7588–8786 500 North Central Еier 3 8787–9685 600 Northeast 9686–10181 700 Western 10182–15876 800 North > 152877 900 Note: These are approximate prevalence values subject to calibrating measuring device(s) Table 2 presents how to assess the mean annual precipitation (MAP) in (mm × 100) and the mean annual potential evapotranspiration (MAE) in (mm × 100), an aggregated annual index (AI) values of 5' grid was used with a combination of the spatial average of the 100 cells at a 30-arcsecond resolution within each 5-arcminute grid cell. Afterward, the spatial average of AI-generated was multiplied by the 10000 factor the moisture content of the soil and the plant evapotranspiration rate. A study by Mueller et al. ( 2012 ) used the above mathematic model in a similar study to access terrestrial surface-covered major food crops investigated in this study examples of such staple crops include; yam, cassava, sweet potato, rice, wheat, rice and sorghum. These are peculiar crops found in SSA soil which constitute as main source of grain, cereals, and tuber crops in the study regions. The study cuts inclusion of areas with negligible crop production, through grid cells with summation of major crops yield > 0.5% of the grid cell area were accounted for, based on Harvest Choice SPAM crop distribution maps as updated frequently under geospatial crop distribution dataset (Monfreda et al. 2008 ) Declined in cropland yields and availability of water for future food crops in the region was and were analyzed. $$\:\begin{array}{c}NDVI=\:\frac{NIR-Red}{NIR+Red}\:\#\left(3\right)\end{array}$$ $$\:\begin{array}{c}NDWI\:=\frac{NIR-SWIR2}{NIR+SWIR2\:}\#\left(4\right)\end{array}$$ $$\:\begin{array}{c}\:NDWI=\frac{G-NIR}{G+NIR}\:\#\left(5\right)\end{array}$$ Where NIR, Red and G are the near-infrared, red and green channels respectively with values ranging from − 1 to + 1 in percentage representing crop growth index and accessible water for food production. However, the normal range for green vegetation is from − 0.1 to 0.4, while water bodies take values from 0.2 to 1, (Taloor et al. 2021 ) (4) and (5) depict the computation of water in crop leaves (plant moisture content) while (5) water bodies for irrigation. Result and Discussion Global Cropland CO 2 emission quota and its effects on the climate. It was observed, the low turn of crop productivity in SSA countries is more concern now than ever with its high emission capacity from the regional unsustainable agricultural practice compared to agricultural industrialized nations. (Fig. 4 ) Figure 4 Global comparison of CO 2 eq/kg emission intensity from cereal crop production between Europe, Asia, and Sub-Sahara Africa. Note the years were displayed on the x-axis due to the complexity of the data analyzed in the system however, it does not affect the result obtained. The study presented the world cropland emission index (Fig. 4 ), illustrating the top GHG emitters nations. The results attempt to justify why SSA countries continue to remain in the list of top GHGs embittering continents and it has been assumed that it is due to high organic waste as one of the characteristics of less developed countries. Inasmuch insignificant environmental economic beneficial seen from the emitting sectors, as they couldn’t justify the economic reasons for annual increases in GHG emitting index from the last 32 years ago among identified global (Ethiopia, Germany, India, Nigeria, Poland, and South Africa) CO 2 emitters from cereal crop production. The result presented above shows SSA (South Africa, Ethiopia and Nigeria) are leading GHG emitters compared to developed and developing countries Germany and India respectively. The situation could have been attributed to the tropical-intensive nature of the climate in the region (Mitra et al. 2022 ). According to the result as shown (Fig. 4 ), between the six (6) countries investigated, South Africa was recorded as the highest emitter of GHG and it equivalent in kg ( CO 2 eq/kg ) between years 1988 to 2020, with its peak annual emission in 1990 that stood at 64kg, followed by 2017 with the value of about 58kg. Between this period its lowest value was seen in the year 2016 with 40kg value. The second highest emitter was identified to be Ethiopia with an intensified emission of these gases in the year 2007 reaching 24kg, then slow down afterwards till the years 2020. The third highest emitter was Nigeria, still came from SSA country with its emitting index far above GHG from developed countries like India, Germany and Poland, with each annual cropland production in term of staple crop yield turnover more than the yield combination of the two (2) SSA (Nigeria and Ethiopia) countries. There is no doubt the intense weather condition in SSA is the key reason for such an escalating high value of CO 2 emission as its major component of greenhouse gas (GHG). It’s possible criticize this finding base on the fact SSA region cereal production is still at a low key compared to India, Germany, and Poland these countries should be hold accounted for higher emission compares to it actual CO 2 eq/kg value. There is assumption of interplay of unsustainable land management in crop production in the region as considerable factor to the high emitting index in SSA. To validate this, it has been confirmed that there was a decline in emission rate from Africa between 2001 to 2016 due to recent conscious steps in the region to reduce grassland and savannah fires (Wojewodzki et al. 2023). The result shows the fact that SSA is in tropical region harsh climatic conditions are inevitable, hence there is a need for the UN agencies to step up efforts to support the mile effort made in the region to diversify their climate response strategies with the core objective to alleviate poverty and reduce excessive emission from croplands through irrigation infrastructure and developing resilient high yielding crops with short production cycle. The quantifiable measures to weigh the adverse effect of climate change on cropland. Existing findings share the same problematic view on the measurability of the climatic chaotic impact on the food sector in SSA nations. (Table 3 ) Table 3 Quantifying the impact of climate change on cropland productivity Variant Indicators Impact Affected SSA Countries Author(s) Higher temperature and water level Insect migration and excessive weed growth 2019–2020 locust infestations affected 1.25 million hectares of cropland. Ethiopia, Kenya, and Somalia (Bold et al. 2017 ) Rising temperatures and rainfall volatility Dwindling of seasonal yields from arable Land. reduced production and impeding total productivity of cropland All SSA countries (Bloem et al. 2010 ) Drought Killed more than 1.5 million livestock drastically cut cereal production All SSA countries (Fry et al. 2009 ) Acidification weighs on agricultural yields Ocean acidification and rising temperatures are shrinking ecosystems shortages of fish, Meat and dairy diminished animal grazing areas, lifespans and impaired embryonic development. All SSA countries (FOA 2023 ) Weak nutritional value Reduction in fish production. Decline in fisheries-related jobs Coastal West Africa (CWA) (United Nations Children’s Fund (UNICEF) 2023 ) Table 3 reflects the deleterious impact of climate change in SSA predicted earlier by another scientist who shared the same view with Ceccarelli et al. ( 2010) in confirming that in 2050, coastal West Africa (CWA) will be cut off with 21% fish production which will be detrimental to farmers and prospects for stable food in the region. Lake Tanganyika will be the most affected, this will further trigger high unemployment with adverse consequences to be felt by East Africa like the Democratic Republic of Congo, Zambia, Burundi and Tanzania. Moreover, the uncommon impact of instability of climatic conditions in SSA sends a strong negative signal for impeding food insecurity as soil productivity and crop yield reflect a lack of agricultural resilience in the region. The investigation of major emitting cropland and their productiveness was done to underpin the area for possible future special intervention. The north centre Tier 2 Nigeria, Niger and 3 Sudan and Ethiopia (Table 1 ; Table 2 ) above were mapped to underpin nations with high propensity with averagely collectively total cropland of 10881657.5 square hectares of land for cereal crops like cowpea, bean, sorghum, and groundnut production which are supported with the crop’s values and cultivable land (Fig. 5; Fig. 6 ) respectively. However, the policy direction for the above-mentioned countries is a major challenge since the inability of SSA climate stakeholders to tackle it effectively has not yielded meaningful results, this gives a clear signal that this is now a global issue. The low yields recorded as presented in Fig. 5 from available cropland presented in Fig. 6 could be attributed to the over-dependency of the region on rain-fed crop production. This in the long run has exposed the soil to volatile rainfall rising temperatures and drought (Cotter and Sauerborn 2012 ; Dell et al. n.d.). Other external challenges in SSA include the lack of proper drainage channels to empty the flooded water in the cropland as well as poor storage capacity farmers due to unstable electricity supply leading to huge quantity of yield either deterioration or loss in its vigour, thereby leading to low germination potential (Dieppe et al. 2020 ). Impact of Climate Change on SSA Nations crop productivity. Reintegrating the UN Food and Agricultural Organizations’ (2020–2021) recent report, with the study results as shown (Fig. 7) is evidence of the decline in some crop production that also portrays instability in its annual production circle within four SSA regions namely the west, central east south, and eastern part of SSA on the dominant crops from 2016–2021 with the constant value from 2013–2016, As shown in Fig. 7, the dwindling gross production index (GPI) value within the four SSA countries (Nigeria, Kenya, Burundi and South Africa) is assumed to reflect the state of food insecurity and hike in GHGs as climate variability intensifies. Inasmuch climate prevalence in SSA nations, it will not be an oversight the strong environmental economic negative impact of the outbreak pandemic (COVID-19), as it coincided with the period of the investigation, hence it was discovered knocked down and the already prevailing high volume of these GHG gas further derailed the efforts of farmers for better yields, leaving households with severity of natural resources dwindling, and diminishing. The consequences is obvious showcasing more hunger and poverty index in the region between 2019–2020, with a significant reduction in cereal crop yields per kg (Fig. 4 ). During this time Africa's food security was disrupted, and it tells how vulnerable SSA farmers stand at the mercy of climate change in the region. However, different studies have proved the innumerable impact of climate change is a global menace to human health (Ofremu et al. 2024 ). This shows (Fig. 7d) that cropland in the region's productivity depends on climatic conditions as its variability poses a serious challenge not only to the food sector but also to the environment as proven (Fig. 6 ) as a lead global GHG emitter. Furthermore, (Fig. 7a), shows that Nigeria's GPI value from 2016–2021 is the function of climate conditions under the same inputs and resources. This means that the same size of farmland may not produce the same specific quantity of crop as it produced in previous years or upcoming years as projection is uncertain when looking at the perilous impact of climate on the vegetation. Furthermore, the results presented (Fig. 7a), shows that Nigerian well-known green garlic producer experienced a sharp slope in their production in 2016. The gross production index (GPI) was about 80 whereas other medicinal crops like ginger green seem to have favourable climate conditions in 2021 with an approximated GPI of 180. Although the result shows there was a steady increase in ginger green from 2017 when its GPI of 100 it keeps increasing, in fact between 2017–2021 GPI increased by 80%. Moreover, the effect of climate on crops seems to have a different impact on different crops with different seasonal quantitative and qualitative values, causing inaccuracy in yield prediction. Specifically, the result codified that Nigerian farmers will be in the frontline of yam production in the absence of climate threat, while Mali's counterpart takes the lead on Maize production. However, the annual yields of both countries are subjected to climate change, essentially water availability and market price. In the East Africa region, starting with Kenya, the result uncovered that in 2013 the country recorded its lowest GPI (0) value whereas between 2020 and 2021 it rose to the peak value of 700, this makes it more complex for the farmers to set profit margins with a level of uncertainty of climate impact on their yields. In the central Africa region, Burundi was reported to experience a tensive negative climate impact on yam production with GPI with the lowest value in 2013, it assumed the situation will significantly affect the country's staple food availability for households in coming years. In southern Africa, the result equally indicates that South African cropland recorded the lowest GPI in 2014 due to the prevailing climatic impact on the vegetation. These findings aligned with an intergovernmental panel on climate change (IPCC 2023 ). In their discovery, it was unfolded how climate change triggers detrimental impacts on low latitude crop yields from 1–2°C local warming. The persistence of this situation may take SSA backward towards attaining SDG 13 which will worsen the milestone success achieved already in SDG 1, 2 & 8 zero hunger, no poverty, and decent work and employment opportunities for Africans. Implication of Climate-Triggered GHGs on Cropland There is a need for evidence-based environmental policies to enhance feasible smart agriculture practices to encourage farmers to reduce greenhouse gas (GHG) emissions as presented in Fig. 8 . The real situation on ground across the savannah belt and grassland region of the SSA nations is deteriorating. This situation posed an ecological imbalance, with fast annihilation natural resources in the continent. It was identified that most of the fertile soil constituting between 58–80% of agricultural land is neither cultivated nor tilled for other use, as the lands lay in waste (uncultivated, used for economic benefit) due to inability to manage climate crisis in the Saharan region of this continent. It unveiled further, that the countries under SSA which faces numerous risks due to severe variability from climate can only depend on rain-fed agriculture as the cropland are not sustainably manage enough to yield maximum output, this assertion is also supported by an existing similar case study in the region (UNEP-UN Envoromental program 2023). The vulnerable countries are in north central Tier 1, 2, and 3 which are Ivory Coast, Togo, and Nigeria, respectively. In addition, some parts of SSA located in Eastern Africa, namely, Somalia and Uganda, and the southern part it’s just South Africa are all included. Unfortunately, space surveillances show that the majority (58%) of massive land still lies uncultivated due to its loss to climate catastrophe, rendering its values between 0–40% usefulness for food production. (Fig. 8 ) 8 (Fig. 9 ) The nexus between unemployment caused by harsh climatic conditions and its triggers of GHG emissions is a crucial challenge, not just to the food sector but to safe surroundings. This deters the mode and systematic approach to crop management, production and processing proves detrimental to future food and poses an environmental threat through its continued emission of GHGs and the region currently runs short of capable technological response hence dependency on rainfed agriculture is inevitable. These and other related constraint factors have compounded the low turnout of the soil (Fig. 8 ), slow production circle and raise the burden of the high cost of production as peasant farmers cannot afford to irrigate their farms. Studies in the continental in comparison with agriculture-revolutionizing nations prove Irrigation practice across the world is pertinent for a successful food transformation all year round to attain food security and an overall economy boost (Bashir and Kyung-Sook 2018). Recent discovery shows that the majority (89%) of nationally determined contribution (NDC) comes from developing countries light SSA nations as the result of unsustainable agricultural operations, the more devastating issue is, that only very few NDC emitters quantified actions to reduce GHG (FAO 2016 ). Moreover, the insufficient farm settlement energy supply makes it almost impossible to automate farming operations and enhance both on-farm and off-farm management efficiency. The situation lingers to unstable employment status (Fig. 9 ) according to the study with evidence of high unemployment within countries like Nigeria which seem to have the highest quota of employment slots annually from agricultural-related jobs. Nigeria faced a decline in the volume of opportunities created through agricultural ventures in 1989–2003 and from 2005–2011 as well as 2014–2019 Afterward, it gained its peak employment record before declining which could be due to the upsurge of Covid. 19, South Africa is one country that has tried to remain stable in its agricultural employability plan but faces constraints due to unseasonal climate variability as a result this has resulted in seasonal lay-off workers to meet up with viable agribusiness. The same case applies to Cameroon, Kenya, Rwanda and Gambia. This result amplifies why UN SDG 8 decent work and economic attaining have been difficult couples of progressive population growth in Africa. An investigation by the World Bank ( 2022 ) and FOA ( 2023 a) shows SSA’s agriculture depends heavily on climatic conditions in the region, with the majority of employment opportunities anchored on agriculture for overall economic prospects. Seasonal variability of rainfall patterns, rising temperatures, floods, and droughts thereby causing a great challenge on food security through decreasing crop yields, animal losses, and rising food prices. It was pertinent to note that the causative factor that led to a drop in cropland productivity is higher temperature leading to an acceleration of the phenological cycle. The situation raised the critical question of the uncertainty of future food in the region as agriculture faces environmental threats caused by climate change, intensified by GHG. The recent report (COP 28 2023 ) United Nations Framework Convention on Climate Change (UNFCCC) presented a pressing issue for agriculture, necessitating strategies like carbon credits to mitigate emissions and enhance productivity. One of the joint signatories proposes a solution is carbon credits, a way forward for reducing emissions. Sustainable Approach to combat climate change & Actionable Step towards safeguarding future food in SSA To combat the impact of climatic action on cropland SSA, the study explores all available response options suitable and has a proven track record to suit African soil. This will be deplored in a holistic approach with local implementor, involving all stakeholders at local, national, and international corridors. (Table 4 ) Table 4 Sustainable pathway to Combat Climate change in SSA. Mitigation Method of application Effectiveness & Efficiency of the measure Sustainability of adoption in the region(s) Author(s) 1. Promoting agroforestry A proven study shows it slows down and, in most cases, reverses cropland degradation. It also sequesters atmospheric carbon while securing ecological services for rural livelihoods. Agroforestry is the most resilient viable nature-based approach available to SSA. Agroforestry will continue to play a vital role in a landscape-scale mitigation scheme to attain SDG 13, 1, 2 and 8 if fully harnessed in SSA. This strategy is eco-friendly and supports nature restoration. It also helps to improve soil nutrients. (Vermeulen et al. 2012 ) 2. Incorporate climate-smart farming Farmers can use climate forecasting tools. It provides accurate and reliable information about climate to save farmers from production catastrophes. This situation is adverted by taking a warning from the gadget which includes the following parameters: earliest planting final planting and prospective sales. Continue to play a role in risk management adaptation helping farmers reduce climate-related production vulnerability. (Durlacher 2023 ) 3. Building resilience across cropland landscapes It has provided mutual benefits for climate change and mitigation through enhancing soil. Carbon sequestration. This mitigation strategy is also active while preventing cropland erosion and runoff. It resuscitates and improves forest vigour while limiting wildfire. Also, it plays a great role in carbon sequestration toward sustainable ecological services. (USDA 2022 ) 4. Raising awareness and training farmers about climate-smart adaptation strategies Educating, training and empowering the public on the method of its application. This has been used to promote the adoption and application of climate-smart adaptation strategies in the United States of America under the United States Department of Agriculture (USDA) and it has proven track of success in replicated regions around the world. United Nations FAO under USAD, NGO pledges to continue to support countries that effectively adopt and implement smart climate strategies. (Durlacher 2023 ; USDA 2022 ) 5. Develop climate data access points for all SSA regional farming centres. By gathering accurate scientific information on climate change. Timeline translating the data into user-friendly information. For decisions to be effectively deployed by environmental policymakers as tools, and models. This makes climate data available and accessible to all climate stakeholders. It also will help to buffer forest health by checking out carbon stock change thereby enhancing health and productivity. It will be easy for Forest Inventory and Analysis (FIA) to ensure forest data is kept, to facilitate the monitoring and possible sustainable long-term prediction. (World Bank 2021 ) 6. Increase investment and joint research support for the development of climate-smart practices and suitable technologies in the region. Championing the coordination, of research agencies' work relating to climate innovational discoveries. Example Zambia has been recommended to focus more on diversification, commercial horticulture, and agroforestry. The NGO, intergovernmental, and regional government synergy fund will go a long way to bring in a quiet response to climate change in SSA. The climate project is not an individual task it requires collective efforts to achieve the climatic agenda of UN SDG 13 toward the set deadline. Continue evaluating the efficacy of adoption with global barometers which provide an opportunity for climatologists in SSA to broaden their knowledge in the universal context of soil carbon storage and GHG emission reduction research being prioritized. (UNFCCC n.d.; World Bank 2021 ) The presented solution in Table 4 will beacon individuals, corporate bodies and government agencies with see green light. This is because the climatic crisis is a complex and multidimensional ecological challenge. Hence, it is necessary for an integrated and comprehensive approach to tackle, therefore the study proposed the above sustainable approach upon reviewing notable climatic, and environmental ecological scientist feasible findings suitable to the regional relief and economic reality. Among this local farmers' inclusiveness in planning and implementation UN SDG 13 framework and international treaties like the Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC) 2015 with a years’ cycle (Roelfsema et al. 2018 ). The adaptation actions will help African climate stakeholders invest more in climate-safer projects and invent new technologies suitable for the region to mitigate the adverse effects of climate. These actions aim to close the gap between innovative science and technology for climate adaptation with local and international approaches. This report also canvases increased access to relevant climate data in the region and, more importantly training and educating the users on how to interpolate and deploy it concerning the intended purpose of de-carbonization and reduction of GHG towards a green economy (World Bank 2021 ). The overall aim of the mitigation includes; building resilience across cropland through aids and investments in soil, boosting public confidence in the application of climate-smart adaptation through grass root education, strengthening access points for climate data as well as its timeline availability to the end users, and lastly it has been reported that the intensification of support for research and development is crucial to scale climate-smart practices adaption in SSA. Conclusions In this study the greenhouse gas and it equivalent emitting index from cropland among SSA and some develop nations were investigated and the result were recorded in Kg between 1988–2020. It was reported that South Africa was the highest embittered values as follows 64kg and 58kg in the year 1990 and 2017 respectively, the country recorded it lowest GHG value within this period in 2016 with 40kg value. Ethiopia was identified as the second highest emitter, in 2007 it recorded 24kg value of CO 2 eq. Third is Nigeria with a mean contributory value of 21kg CO 2 eq. It was uncovered through the remote sensing (RS) surveillance that the majority of about (58%) of massive land still lies uncultivated due to its loss to climate catastrophe rendering its values between 0–40% usefulness for food production. The study underlined the most vulnerable part of SSA categorizing them into 3 tiers. It was also quantified the total mass area affected and the countries it lies. They include an estimated total cropland of 10881657.5 square hectares in North central Tier 2 (Nigeria, Niger) and North central Tier 3 (Sudan, Ethiopia) these region were mapped nations likely to be confronted with environmental crisis from climate variability in future. On the effect of the emission index on crops yields, it was reported that it led to intensification of unsustainable crop production, as shown in and low production of dominant staple crops such as cowpea, bean, sorghum, and ground, among others. It was forecasted of 21% further losses of ecological resources in future due to agricultural related ecological services losses. The environmental impact was identified which include ecological loss of vital natural resources and fast land degradation is continuing environmental deterioration, food insecurity, and adverse effects of climate change and its variability menace which are been witnessed across the Horn of Africa. The situation has gravitated to causing majority of SSA countries in impeding danger of climate destruction, water risk, and ecological resources diminishing annually from 2013–2022 investigated. The economic impact has been witnessed in some nations like Nigeria, South Africa, Kenya, Mali and Burundi, Zambia with an outnumbered population unemployed due to significant agricultural-related job loss annually as the situation persists. The study recommends an integrated approach that will involve local farmers, extension officers, and specialists who understand the geographical feature to support all through the planning implementation stages of the response measures highlighted which include, building resilience across and cropland landscapes, use of climate-smart farming, promoting agroforestry, raising awareness and training farmers about climate-smart adaptation strategies and developing climate data access points for all SSA regional farming centres. Declarations Ethical Responsibilities of Authors The author has read, understood, and complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors. Ethical approval Not applicable Consent to participate. Not applicable Consent to publish: Not applicable Funding: There is no fund or grant, or any financial support received in this study. Data availability All relevant data are within the paper. Funding No funding was received for conducting this study. Financial interests : None Authors’ contributions Emmanuel Igwe Conceptualization, Methodology, Investigation, Software (RS and GIS) application, Writing–Original draft preparation, review and edit the final draft The author declares no conflict of interest There are no conflicts of interest to declare. Acknowledgements : This work was overseen by my academic supervisor the head postgraduate program at Ecology Institute RUDN University Prof. Daria O. 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Igwe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYLCCBCBmkz98AEhJyBCvhV+CDURJ8BBvk+QMHgMQTVgLv9jpxA8Pfh2WM7jd8/nVjRoLHgb2w0c34DV8du5micS+w8YGd85us845BnQYT1raDXxaDG7nbpBI7LmduOFA7jbjHDagFgkeM7xa7G/nbv4B1FK/4UDOM+Ocf0RoMZDO3SaR8ON2guSMHObHuW1EaJG4nbvNIrHhv2E/zzEz5tw+CR42Qn7hB3r/5o8/afJs7M2PP+d8q5PjZz98DK8WMGBsA1NsEmCSoHIw+AMmmT8Qp3oUjIJRMApGGgAAYd5OGHuUIhcAAAAASUVORK5CYII=","orcid":"","institution":"Peoples' Friendship University of Russia","correspondingAuthor":true,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Igwe","suffix":""}],"badges":[],"createdAt":"2024-10-14 12:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5261257/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5261257/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-025-13633-2","type":"published","date":"2025-01-30T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69418873,"identity":"6966d95c-74f3-4f8a-a6cf-dae4618089b3","added_by":"auto","created_at":"2024-11-20 07:32:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":121434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual diagram illustrating causative factor of climate change and its impact on cropland.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/e738b6493a139b8708c6e3d8.png"},{"id":69420402,"identity":"a9f5a07b-1cb4-4049-a78c-cfa9243eec47","added_by":"auto","created_at":"2024-11-20 07:40:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":182556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChart flow of impacts of climate crisis in the ecological sphere.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/ad685cd80a74c66c7ea8557f.png"},{"id":69420399,"identity":"7e5f0841-edc9-4be5-af9d-cf83d2260c36","added_by":"auto","created_at":"2024-11-20 07:40:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":245854,"visible":true,"origin":"","legend":"\u003cp\u003eSSA climate zones, Source: (Atlas n.d.)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/3c10875c71c3c222a2665db4.png"},{"id":69421060,"identity":"49d4d2cf-6fe6-4541-ae11-7e5579e5413d","added_by":"auto","created_at":"2024-11-20 07:48:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":45178,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal comparison of CO\u003csub\u003e2\u003c/sub\u003eeq/kg emission intensity from cereal crop production between Europe, Asia, and Sub-Sahara Africa.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/bfa5258a4731cbb773d0ead8.png"},{"id":69420398,"identity":"002b2576-8999-4acb-85e8-9bc724891bca","added_by":"auto","created_at":"2024-11-20 07:40:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135756,"visible":true,"origin":"","legend":"\u003cp\u003eMean crop yields in SSA from 1988-2021\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/5a2a0445d97d0c263ef3a978.png"},{"id":69421062,"identity":"2ae448da-4d8c-4832-a1a6-90b0f43ff887","added_by":"auto","created_at":"2024-11-20 07:48:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":193116,"visible":true,"origin":"","legend":"\u003cp\u003eMean Cultivable Cropland in SSA from 1988-2021\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/5cc6aede97a3eec17495c5df.png"},{"id":69422442,"identity":"3ae90450-8c76-480c-9fbf-384a821b3b26","added_by":"auto","created_at":"2024-11-20 07:56:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1011165,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the gross production index (GPI) of East, West, Central and Central Africa 2016-2021\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/25697725e2c0f94c708139bb.png"},{"id":69418868,"identity":"78e910cc-997f-4f4c-a7a0-dcbfdfa44fea","added_by":"auto","created_at":"2024-11-20 07:32:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":212637,"visible":true,"origin":"","legend":"\u003cp\u003eProduction index in SSA from 1988-2021\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/fded1d3a28f68b9910f00cb1.png"},{"id":69418863,"identity":"7b8d252e-a7f1-4298-9551-af43896757ca","added_by":"auto","created_at":"2024-11-20 07:32:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":56879,"visible":true,"origin":"","legend":"\u003cp\u003eThe impacts of GHGs on environmental-economics opportunities in the SSA\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/50d7aaae43a93862b3be4f95.png"},{"id":75351220,"identity":"305e3805-2ffb-4653-969b-766dba4fcb1f","added_by":"auto","created_at":"2025-02-03 16:08:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3196332,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/ed8b89fd-f9de-4768-aaed-9538bf6ab6bf.pdf"},{"id":69420401,"identity":"1c02e93e-f053-444f-9407-57701d69aff9","added_by":"auto","created_at":"2024-11-20 07:40:54","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":313556,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/ac6b99e41ede3bf7addcf489.jpg"},{"id":69418870,"identity":"05d86d06-93d0-404f-92e3-2a7ea3f9cf2b","added_by":"auto","created_at":"2024-11-20 07:32:54","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":114415,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5261257/v1/233e945c3d56dc00e1442599.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Assessment of Sub-Saharan Africa's GHG emission from cropland in comparison to some developing nations, its environmental economic impacts, and mitigation measures","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThere are urgent risen global concerns about the unabated climate crisis in the Sub-Sahara Africa (SSA) countries caused by anthropogenic activities, and the scenario tends to cripple the natural resources availability with eroded soil, thereby undermining natural-based resources efficiency (Alewell et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mc Carthy et al. 2018), Unfortunately, there exist insignificant scientific response measures to curtail this now, hence the situation led to an intensification of food insecurity across SSA and the rest of the World, as SSA is considered a major food producer in the continent. The enormous food wastage (deterioration, loss) has risen annually as the climate continues to pose global concerns that question the survival of human existence in the region, and the adverse effect on food security has become inevitable since the decades ground effort so far in curtailing ravaging diminishing natural resource seems (Brenya et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Successive shocks from the COVID-19 pandemic have worsened the food scarcity in the region. According to the Global International Monetary Fund (IMF), this backlog has significantly increased by not more than 30 percent since early 2020 (Mitra et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The implication is that about 12% of the entire SSA population in 2022 suffered severe malnutrition, hunger, and deaths in some critical situations. The SSA over the years has continued to witness climatic catastrophic events that trigger unexpected drought, cyclones and floods occurrence, high Land Surface Temperature (LST), and rivers submerging, all these dilapidating situations hasten biodiversity losses, setting back UN SDG 1 and 2 targets in the region (Muza \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Quartey et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These come with no surprise as studies have shown such an unprepared major climate event, people die of hunger and the survivors are less productive, already achieved response measures are thwarted, thereby upsetting the systemized operational economic activity, destabilizes ecological management order as seen recently even in fast-developing countries like China were little can be done at this critical point, except a resilient and quick measure can salvage such situation (Wu and Wang \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Efforts towards assessing the impact of climate in the SSA ecological niche, need to be understood as a broadened issue that encompasses ecocentrism, biocentrism, and anthropogenic activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The latter has significant impacts as it influences the formal (ecocentrism and biocentrism) while interfering with the niche building.\u003c/p\u003e \u003cp\u003eMoreover, succession processes trigger excessive GHG emitting activities in a process called \"niche construction\" Without prejudice to the existing climate-distorting effects, it is believed that man can salvage this menace by drafting ecologically friendly norms, enforcing them to regulate the interplays of the 3 drivers above which shape the likelihood of detoxification of co-contamination of organic pollutants as well heavy metallic (soil nutrients) leaching into the surroundings. This will be a key pointer to the need for natural environment restoration and resilience (O\u0026ntilde;a et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous studies have proven that these factors impact not just the ecosystem but lives, altering life expectancy, opportunities, and health status of habitat, reflecting the quality of life thereof (Igwe et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTacking climate and its variability over the years has been successively achieved in some developed and developing countries (USA, Germany, Russia, India, and China) which SSA countries can replicate based on their regional climate and relief adaptability. Remote Sensing (RS) and Geographical Information Systems (GIS) have been reported to be game-changers in controlling and regulating activities of crops and the field, help provide real-time information that guides food decision-makers, environmentalists, monitor anthropogenic activities and determining appropriate responses to climate change while keeping to sustainable intensification of agricultural operation to proffer solution ineffective climate policy (FOA \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A similar study conducted in SSA countries estimated the land surface temperature (LST) to be alarming (Sajib and Wang \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The continuous man disruption and its process are illustrated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). LST is the measuring metric to determine the thermodynamic skin cropland by measuring the infrared radiation coming from the surface, its specialized method to examine urban heat islands, monitored crop growth and for detection of forest fires (Eckmann et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhou et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, it can also be used for related studies like hydrological processes with the application of the principle Normalized Difference Water Index (NDWI) which is useful in assessing the intensity of climate on cropland through its LST (Crow and Wood \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Again, regulation of environmental limiting factors from GHG emission as well as its interaction with inhibitor plants hormone, gibberellin and xylem which controls evapotranspiration rate to enhance crop maturity rate (Aslan and Koc-San \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Crow and Wood \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Kustas and Anderson \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Shwetha and Kumar \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It's important to highlight here basic cropland parameters (air temperature, relative humidity, soil temperature, activities of microflora and microfauna) which reflect the activity rate of soil microorganisms is crucial for a better understanding of the indexing rate of GHG from an agricultural perspective, and better propose sustainable responses to tackle it in the SSA as well as at the global level.\u003c/p\u003e \u003cp\u003eThe assessment of the current state of food production in this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) shows the series of ecological breakdowns caused by unsustainable direct and indirect agricultural activities which hastens soil degradation, either erosion, salinization, or bogging due to poor management practices (Andersen 2024). Sequential stages of it led to unabated GHG emissions with its reaction bringing more disbalance in the ecosystem, derailing its restoration potential, reduced essential minerals that support crop production. In extreme soil temperature situations, it can escalate the evapotranspiration rate thereby causing low soil moisture, crop wilt and deterioration of perishable produce in the absence of storage facilities. Based on the UN Food and Agriculture Organization's (FAO) recent findings (FOA \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003ea), SSA suffers annual 38 kg ha\u0026thinsp;\u0026minus;\u0026thinsp;1 (i.e., 26 kg N, 3 kg P, 9 kg K nutrient depletion with a heavy burden on soil infertility. As demonstrated in Figure (2), climate change takes credit for tremendous damages done not just to food security but also to the overall environment outlook, and human health. According to the World Health Organization (WHO), the state of the surroundings contributes up to 18\u0026ndash;20% of the populace's health. This makes it impossible to achieve speedy healthy population growth in the absence of food security as food nutrient deficiency in the daily diet of humans gradually exposes them to certain diseases such as kwashiorkor, fever, low mental development, and high mortality rate at the same time exposes the crop to some seasonal pest's infestation. In addition, crop growth and development depend mainly on soil nutrient availability and the percent of moisture. Even though most of these nutrients can be contaminated with metallic ions according to my previous studies this situation emanates from unsustainable agricultural activities in Africa (Igwe et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003ea). Notable research proves frequent heavy rainfall can harm crops by eroding the soil surface causing essential plant nutrient leaching (Gowda et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The continuity of these situations can lead to soil runoff (agrochemicals) into oceans thereby contributing heavily to freshwater water contamination with heavy metallic ions (Igwe et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003eb). In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e the study highlighted the trends of climate and its variability and demonstrated how it can spark GHGs emission triggers and impact food insecurity anywhere in the world, with the outcome of acute hunger and severe poverty. The challenge of adopting smart agriculture technologies in the region has aggregated the climatic risk impact on household farmers' depicting their incapacities to invest in agriculture food innovation, value addition, integrated farming, and ecological services to support organic farming, hence they keep recycling old systems of agriculture, promoting further climate variability in the region (Wakweya 2023).\u003c/p\u003e \u003cp\u003eConsequently, an increased food insecurity could jeopardize the hard-earned improvements in the incomes, education, and health status within the African continent. Recent studies show that Peri-urban food insecurity and coping strategies remain an understudied topic in SSA (Haile Aboye et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These and other serious humanitarian and economic implications could spark conflict and large-scale migration, which calls for urgent actions to save the rampaging loss in biodiversity due to the climate change emanating from excessive emitting of GHG. Further claims show that one-third of the global droughts occur in SSA, this makes the region the most vulnerable part of the world that is prone to food insecurity, coupled with limited technologies, and a high index poverty ratio which summed up with complicated and weakening efforts to reduce GHGs ineffective (World Bank Group \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another scenario is the continued removal of forests in addition to the already experienced desert encroachment in the northern part of SSA like Niger and Chad makes the situation more grievous, causing the source of livelihood more difficult to access. The condition has been triggered on SSA cropland with long ages loading of the lithosphere, atmosphere, and hydrosphere with excessive GHGs such as nitrous oxide (N2O), methane (CH4) spillage, and other destructive chemical from agricultural runoff into the cropland which enhances GHG build-up resulting in 265 times the global warming potential of carbon dioxide (CO2) per mass over a 100-year time horizon (Uen and Rodr\u0026iacute;guez \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe consequential impact of anthropogenesis drives the negative effect of today's environmental and food crisis as shown.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn another context, the climate crisis has the propensity to make conditions better or worse for growing crops in different regions, especially in water risk-prone areas according to RS with aided GIS surveillance as presented a schematic erupting process and stages of human-induced climatic change (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Recent study shows slight changes in temperature, rainfall, combined with frost-free days conditions tends to prolong crop growing seasons (Gowda et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although a longer growing season has two coins, both positive and negative impacts on the food production cycle, as some farmers have working capital to wait for their yields for the plant longer-maturing crops (annual crops), while others may not, resulting in more irrigation over a longer, this can lead to high cost of production. Moreover, the situation may warrant long-term greenhouse gas emission potential which can endanger the surface air, damage crops, plants, forests, and loss of important species of biodiversity (Akon et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, there are some scientists with a different view on the approach to tackle this discourse, they argue that there is no proof to address the climate challenge to regulate the GHG emission index since the causes are multi-complicity and interconnected (McCarthy et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), hence in their view it almost impossible to lay hand on a single approach to tackle the impact of climate on the nature. Currently, ground breakthrough findings by renowned climate scientists on climate in the pile the line (Hansen et al. 2023) support an integrated approach to tackling climate challenges. The rich agricultural land in Sub-Saharan Africa demands responsible stewardship to promote soil fertility, ecosystem balance, and biodiversity conservation, ensuring long-term food security for local farming communities. It seems infeasible due to the climate emergency couple's poor responses and a low technology upheaval to sustainably revolutionize food production, (Kihara et al. 2020), hence this study considers it as one of the critical issues to examine climate vulnerability in instigating high GHGs and proffer possible remedy.\u003c/p\u003e \u003cp\u003eThe novelty of this research is the determination of the proportional cropland with a high GHG index in SSA countries secondly to equate the cropland land and its productiveness as influence my climate changes and its variability effects on the economic status of the populace. Afterward, it provides grassroots local involvement and foreign response driven by synergetic intervention based on the ecological and geographical compatibility of the soil in the study area. This will hopefully bring the long-term desire for sustainable agricultural transformation and corresponding quick ecological restoration to mitigate seasonal excessive GHG emissions sparking climate change during the crop production cycle. This is essential in the region as research on the topic is limited. This paper is organized as follows. \"Approach and Method\" This section describes the method and process of how data were collected and analyzed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eThe study deploys an application of remote sensing (RS) Sentinel-7 near real-time (NRT) data to assess the land surface temperature (LST) effects on normalized difference vegetation index (NDVI) as well as normalized difference water index NDWI from within the study area. Panel data were used to statistically analyze the Global Warming Index (GWI) in contrast with SSA, and the impact factor on dominant crops in the regions (southern, central, and eastern) coast of African countries that made up SSA nation within 1988–2022. The empirical measurement of the emission index based on panel data was determined in CO2eq/kg, while the crop losses were weighed in kilograms per year. The effect of the losses was evaluated by the number of agricultural-related job losses in the study period. The study deploys spatial production allocation model layer access to the data generated from RS and GIS due to the peculiar fragmentation nature of cropland existing in SSA countries' farmlands. Hence the approach involves strategic steps (mapping 2 agrarians in 2 states each from the southern, central, and eastern parts of SSA), and evaluating crop losses from climate variability. To validate the information, collect and process the study employed the services of expert knowledge from agronomists and the International Institute of Tropical Agriculture (IITA), and the global cropland emission index was conducted from data of global cropland monitoring tool (v1)(Tools \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) relevant literature on existing climate responses with similar case studies was used to proffer remedies based on the study findings. These provide the study easy access to real-time and secondary data used to identify the dominant crop cultivated area and support the study justification on the discrepancy from yield losses per year due to excessive soil temperature and evapotranspiration. The entire vegetation in the SSA study region was categorized with pixels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) which were grouped into four categories and were assigned a group under sparse vegetation, shrubland, barren lands, forest, and grasslands (savannah vegetation) following NDVI pixel classification range (Nordborg et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Usťak et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and quantified abandoned cropland by comparing individual pixels of the ESA CCI-LC and C3S-CDS datasets for all years between years of study.\u003c/p\u003e\n\u003ch3\u003eStudy area\u003c/h3\u003e\n\u003cp\u003eSub-Saharan Africa (SSA) is the African continent's southernmost region, made up of forty-seven (47) out of 54 African countries which include; Nigeria, Cameroon, Namibia, South Africa, Mali, Senegal, Ghana, Gambia, Rwanda, Zambia, Uganda, Zimbabwe, Sierra Leone, Niger, United Republic of Tanzania, Togo, Swaziland, Seychelles, Mozambique, Madagascar, Sao Tome and Principe, Guinea-Bissau, Kenya, Somalia, Mauritius, Lesotho, Equatorial Guinea Eritrea, Benin, Democratic Republic of the Congo, Botswana, Burundi, Chad, Liberia, Burkina Faso, Comoros, Malawi, Gabon, Côte d'Ivoire, Central African Republic and Cape Verde among others (UNEP-UN Environmental program 2023) with exclusion of Algeria, Egypt, Morocco, Somalia, Sudan, and Tunisia. The continent is the second largest in the world after Asia and it is unique and distinct both geographically and ethno-culturally. It is known for its massive cropland which serves as the main source of economy and foreign exchange to date. Almost the entire SSA is in the tropics and displays tropical and subtropical climatological zone characteristics.\u003c/p\u003e\n\u003ch3\u003e(Fig.\u0026nbsp;)\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure. 3 represents about 70% of the landmass of the continents of SSA that lie between the tropics of Cancer and Capricorn respectively positioning the region to experience a tropical climate. These geographical positions instigated the highly vulnerable rate of food production in the region (Misiou and Koutsoumanis \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and this has negatively compelled farmers to overreliance on rain-fed agricultural production. Notably, the adverse effects of the above have been reported in recent studies to susceptible vagaries of weather and climate, continuous low outputs, and fluctuations in agricultural market shares and food commodities and also threaten natural lifecycle (Elias \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wheeler and Von Braun \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGHG Emission Computation Description\u003c/h3\u003e\n\u003cp\u003eThe study adopted two systematic computation processes (i) stochastics frontier analysis (SFA) to examine panel data from 1988 to 2020 and (ii) one EX-ACT to estimate the GHG balance at the level of each region over the simulated 32-year period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) in one scenario (optimistic AP adoption) with simulation each afterward comparison was conducted among the global emitter while the economic impact was measured in terms of yields loss per Kg like per Kg GHGs emission index. EX-ACT boasts advanced tools for estimating GHG emissions over extended periods, offering a distinct advantage over Tropical farms' one-year assessment timeframe. This calculation was developed by a UN Food and Agriculture Organization (FOA) expert since then this GHG calculator has been in use (Bernoux et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) for similar cases in computing CO2 and its equivalent gas emissions.\u003c/p\u003e \u003cp\u003eMeanwhile, data were assembled from a panel dataset consisting of a cross-sectional and time-series dataset compared with RS and GIS Satellite data generated from the land surface temperature (LST) air temperature, precipitation, and relative humidity. The time frame spans from 1988 to 2020. This is followed by the description of the study area which unveiled geographical features such as relief, climate, and types of vegetation understudied. To effectively monitor the emission index and its effects on crop yield the study uses remote sensing and geographical information system (AFROCOVER) and Sentinel-7 and 3 to access data for the period under investigation. Afterward, the study is presented in the \"Results and Discussion\" section discusses the obtained findings of this work. Finally, conclusions are drawn in the \"Conclusion\" section.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTemperature Algorithm\u003c/h2\u003e \u003cp\u003eThe reference weather stations for the key production areas of these crops were extracted from the Global Yield Gap Atlas (GYGA), with climatic drive changes recorded using a precise ecological arithmetic model built on secondary data from the World Metrological Organization (WMO). The growing degree days (GDD) values were calculated as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}GDD=\\sum\\:_{\\varvec{i}=1}^{365}\\varvec{M}\\varvec{a}\\varvec{x}\\:\\left(0,{\\varvec{T}}_{\\varvec{i}}-{\\varvec{T}}_{\\varvec{b}}\\right)\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult obtained for Regional GDD and GYGA-CZ Value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDD (°Cd)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGYGA-CZ Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–3669\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth Central Tier 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3670–4164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth Central Tier 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4165–4790\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSoutheast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4791–5830\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth Central Tier 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5831–6949\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth Central Tier 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6950–7211\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth Central Tier 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7212–8964\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8965–9511\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWest\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9512–13250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; 13250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNote: These are approximate prevalence values subject to calibrating measuring device(s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe average density of air and land surface temperature (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was computed using the GDD model (Eq.\u0026nbsp;1) in which T\u003csub\u003ei\u003c/sub\u003e is the temperature (°C) for each time step and is T\u003csub\u003eb\u003c/sub\u003e the base temperature (0°C for our calculations) was used for the computation according to (Licker et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSub-Saharan African Quotient of water\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}AI\\:=\\:\\frac{\\varvec{M}\\varvec{A}\\varvec{P}}{\\varvec{M}\\varvec{A}\\varvec{E}}\\:\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult obtained for LST and corresponding GYGA-CZ Value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurface temperature\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI (-) Mean seasonal Precipitation Range\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGYGA-CZ Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esouth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–3694\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Central Tier 1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3695–4894\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Central Tier 2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4895–5790\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEastern\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5791–6690\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth Central Tier 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6691–7587\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth Central Tier 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7588–8786\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNorth Central Еier 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8787–9685\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9686–10181\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10182–15876\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; 152877\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNote: These are approximate prevalence values subject to calibrating measuring device(s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents how to assess the mean annual precipitation (MAP) in (mm × 100) and the mean annual potential evapotranspiration (MAE) in (mm × 100), an aggregated annual index (AI) values of 5' grid was used with a combination of the spatial average of the 100 cells at a 30-arcsecond resolution within each 5-arcminute grid cell. Afterward, the spatial average of AI-generated was multiplied by the 10000 factor the moisture content of the soil and the plant evapotranspiration rate. A study by Mueller et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) used the above mathematic model in a similar study to access terrestrial surface-covered major food crops investigated in this study examples of such staple crops include; yam, cassava, sweet potato, rice, wheat, rice and sorghum. These are peculiar crops found in SSA soil which constitute as main source of grain, cereals, and tuber crops in the study regions. The study cuts inclusion of areas with negligible crop production, through grid cells with summation of major crops yield \u0026gt; 0.5% of the grid cell area were accounted for, based on Harvest Choice SPAM crop distribution maps as updated frequently under geospatial crop distribution dataset (Monfreda et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eDeclined in cropland yields and availability of water for future food crops in the region was and were analyzed.\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}NDVI=\\:\\frac{NIR-Red}{NIR+Red}\\:\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}NDWI\\:=\\frac{NIR-SWIR2}{NIR+SWIR2\\:}\\#\\left(4\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\:NDWI=\\frac{G-NIR}{G+NIR}\\:\\#\\left(5\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003eWhere NIR, Red and G are the near-infrared, red and green channels respectively with values ranging from − 1 to + 1 in percentage representing crop growth index and accessible water for food production. However, the normal range for green vegetation is from − 0.1 to 0.4, while water bodies take values from 0.2 to 1, (Taloor et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (4) and (5) depict the computation of water in crop leaves (plant moisture content) while (5) water bodies for irrigation.\u003c/p\u003e "},{"header":"Result and Discussion","content":"\u003cp\u003eGlobal Cropland CO\u003csub\u003e2\u003c/sub\u003e emission quota and its effects on the climate.\u003c/p\u003e\u003cp\u003eIt was observed, the low turn of crop productivity in SSA countries is more concern now than ever with its high emission capacity from the regional unsustainable agricultural practice compared to agricultural industrialized nations.\u003c/p\u003e\u003ch2\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Global comparison of CO\u003csub\u003e2\u003c/sub\u003eeq/kg emission intensity from cereal crop production between Europe, Asia, and Sub-Sahara Africa.\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003ethe years were displayed on the x-axis due to the complexity of the data analyzed in the system however, it does not affect the result obtained.\u003c/em\u003e \u003c/p\u003e\u003cp\u003eThe study presented the world cropland emission index (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), illustrating the top GHG emitters nations. The results attempt to justify why SSA countries continue to remain in the list of top GHGs embittering continents and it has been assumed that it is due to high organic waste as one of the characteristics of less developed countries. Inasmuch insignificant environmental economic beneficial seen from the emitting sectors, as they couldn’t justify the economic reasons for annual increases in GHG emitting index from the last 32 years ago among identified global (Ethiopia, Germany, India, Nigeria, Poland, and South Africa) CO\u003csub\u003e2\u003c/sub\u003e emitters from cereal crop production. The result presented above shows SSA (South Africa, Ethiopia and Nigeria) are leading GHG emitters compared to developed and developing countries Germany and India respectively. The situation could have been attributed to the tropical-intensive nature of the climate in the region (Mitra et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to the result as shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), between the six (6) countries investigated, South Africa was recorded as the highest emitter of GHG and it equivalent in kg (\u003cb\u003eCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eeq/kg\u003c/b\u003e) between years 1988 to 2020, with its peak annual emission in 1990 that stood at 64kg, followed by 2017 with the value of about 58kg. Between this period its lowest value was seen in the year 2016 with 40kg value. The second highest emitter was identified to be Ethiopia with an intensified emission of these gases in the year 2007 reaching 24kg, then slow down afterwards till the years 2020. The third highest emitter was Nigeria, still came from SSA country with its emitting index far above GHG from developed countries like India, Germany and Poland, with each annual cropland production in term of staple crop yield turnover more than the yield combination of the two (2) SSA (Nigeria and Ethiopia) countries. There is no doubt the intense weather condition in SSA is the key reason for such an escalating high value of CO\u003csub\u003e2\u003c/sub\u003e emission as its major component of greenhouse gas (GHG). It’s possible criticize this finding base on the fact SSA region cereal production is still at a low key compared to India, Germany, and Poland these countries should be hold accounted for higher emission compares to it actual CO\u003csub\u003e2\u003c/sub\u003eeq/kg value. \u003cb\u003eThere is assumption of interplay of\u003c/b\u003e unsustainable land management in crop production in the region as considerable factor to the high emitting index in SSA. To validate this, it has been confirmed that there was a decline in emission rate from Africa between 2001 to 2016 due to recent conscious steps in the region to reduce grassland and savannah fires (Wojewodzki et al. 2023). The result shows the fact that SSA is in tropical region harsh climatic conditions are inevitable, hence there is a need for the UN agencies to step up efforts to support the mile effort made in the region to diversify their climate response strategies with the core objective to alleviate poverty and reduce excessive emission from croplands through irrigation infrastructure and developing resilient high yielding crops with short production cycle.\u003c/p\u003e\u003cp\u003eThe quantifiable measures to weigh the adverse effect of climate change on cropland.\u003c/p\u003e\u003cp\u003eExisting findings share the same problematic view on the measurability of the climatic chaotic impact on the food sector in SSA nations.\u003c/p\u003e\u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuantifying the impact of climate change on cropland productivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpact\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAffected SSA Countries\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAuthor(s)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher temperature and water level\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsect migration and excessive weed growth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019–2020 locust infestations affected 1.25\u0026nbsp;million hectares of cropland.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEthiopia, Kenya, and Somalia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Bold et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRising\u003c/p\u003e \u003cp\u003etemperatures and rainfall volatility\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDwindling of seasonal yields from arable\u003c/p\u003e \u003cp\u003eLand.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ereduced production and impeding total productivity of cropland\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll SSA countries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Bloem et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrought\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKilled more than 1.5\u0026nbsp;million livestock\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edrastically cut cereal production\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll SSA countries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Fry et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcidification weighs on agricultural yields\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcean acidification and rising temperatures are shrinking ecosystems\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eshortages of fish,\u003c/p\u003e \u003cp\u003eMeat and dairy diminished animal grazing areas, lifespans and impaired\u003c/p\u003e \u003cp\u003eembryonic development.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll SSA countries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(FOA \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeak nutritional value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduction in fish production.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecline in fisheries-related\u003c/p\u003e \u003cp\u003ejobs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoastal West Africa (CWA)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(United Nations Children’s Fund (UNICEF) \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reflects the deleterious impact of climate change in SSA predicted earlier by another scientist who shared the same view with Ceccarelli et al. ( 2010) in confirming that in 2050, coastal West Africa (CWA) will be cut off with 21% fish production which will be detrimental to farmers and prospects for stable food in the region. Lake Tanganyika will be the most affected, this will further trigger high unemployment with adverse consequences to be felt by East Africa like the Democratic Republic of Congo, Zambia, Burundi and Tanzania. Moreover, the uncommon impact of instability of climatic conditions in SSA sends a strong negative signal for impeding food insecurity as soil productivity and crop yield reflect a lack of agricultural resilience in the region.\u003c/p\u003e\u003cp\u003eThe investigation of major emitting cropland and their productiveness was done to underpin the area for possible future special intervention.\u003c/p\u003e\u003cp\u003eThe north centre Tier 2 Nigeria, Niger and 3 Sudan and Ethiopia (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) above were mapped to underpin nations with high propensity with averagely collectively total cropland of \u003cb\u003e10881657.5 square hectares\u003c/b\u003e of land for cereal crops like cowpea, bean, sorghum, and groundnut production which are supported with the crop’s values and cultivable land (Fig.\u0026nbsp;5; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) respectively. However, the policy direction for the above-mentioned countries is a major challenge since the inability of SSA climate stakeholders to tackle it effectively has not yielded meaningful results, this gives a clear signal that this is now a global issue. The low yields recorded as presented in Fig.\u0026nbsp;5 from available cropland presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e could be attributed to the over-dependency of the region on rain-fed crop production. This in the long run has exposed the soil to volatile rainfall rising temperatures and drought (Cotter and Sauerborn \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dell et al. n.d.). Other external challenges in SSA include the lack of proper drainage channels to empty the flooded water in the cropland as well as poor storage capacity farmers due to unstable electricity supply leading to huge quantity of yield either deterioration or loss in its vigour, thereby leading to low germination potential (Dieppe et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eImpact of Climate Change on SSA Nations crop productivity.\u003c/p\u003e\u003cp\u003eReintegrating the UN Food and Agricultural Organizations’ (2020–2021) recent report, with the study results as shown (Fig.\u0026nbsp;7) is evidence of the decline in some crop production that also portrays instability in its annual production circle within four SSA regions namely the west, central east south, and eastern part of SSA on the dominant crops from 2016–2021 with the constant value from 2013–2016,\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;7, the dwindling gross production index (GPI) value within the four SSA countries (Nigeria, Kenya, Burundi and South Africa) is assumed to reflect the state of food insecurity and hike in GHGs as climate variability intensifies. Inasmuch climate prevalence in SSA nations, it will not be an oversight the strong environmental economic negative impact of the outbreak pandemic (COVID-19), as it coincided with the period of the investigation, hence it was discovered knocked down and the already prevailing high volume of these GHG gas further derailed the efforts of farmers for better yields, leaving households with severity of natural resources dwindling, and diminishing. The consequences is obvious showcasing more hunger and poverty index in the region between 2019–2020, with a significant reduction in cereal crop yields per kg (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). During this time Africa's food security was disrupted, and it tells how vulnerable SSA farmers stand at the mercy of climate change in the region. However, different studies have proved the innumerable impact of climate change is a global menace to human health (Ofremu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This shows (Fig.\u0026nbsp;7d) that cropland in the region's productivity depends on climatic conditions as its variability poses a serious challenge not only to the food sector but also to the environment as proven (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) as a lead global GHG emitter. Furthermore, (Fig.\u0026nbsp;7a), shows that Nigeria's GPI value from 2016–2021 is the function of climate conditions under the same inputs and resources. This means that the same size of farmland may not produce the same specific quantity of crop as it produced in previous years or upcoming years as projection is uncertain when looking at the perilous impact of climate on the vegetation. Furthermore, the results presented (Fig.\u0026nbsp;7a), shows that Nigerian well-known green garlic producer experienced a sharp slope in their production in 2016. The gross production index (GPI) was about 80 whereas other medicinal crops like ginger green seem to have favourable climate conditions in 2021 with an approximated GPI of 180. Although the result shows there was a steady increase in ginger green from 2017 when its GPI of 100 it keeps increasing, in fact between 2017–2021 GPI increased by 80%. Moreover, the effect of climate on crops seems to have a different impact on different crops with different seasonal quantitative and qualitative values, causing inaccuracy in yield prediction. Specifically, the result codified that Nigerian farmers will be in the frontline of yam production in the absence of climate threat, while Mali's counterpart takes the lead on Maize production. However, the annual yields of both countries are subjected to climate change, essentially water availability and market price. In the East Africa region, starting with Kenya, the result uncovered that in 2013 the country recorded its lowest GPI (0) value whereas between 2020 and 2021 it rose to the peak value of 700, this makes it more complex for the farmers to set profit margins with a level of uncertainty of climate impact on their yields. In the central Africa region, Burundi was reported to experience a tensive negative climate impact on yam production with GPI with the lowest value in 2013, it assumed the situation will significantly affect the country's staple food availability for households in coming years. In southern Africa, the result equally indicates that South African cropland recorded the lowest GPI in 2014 due to the prevailing climatic impact on the vegetation. These findings aligned with an intergovernmental panel on climate change (IPCC \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In their discovery, it was unfolded how climate change triggers detrimental impacts on low latitude crop yields from 1–2°C local warming. The persistence of this situation may take SSA backward towards attaining SDG 13 which will worsen the milestone success achieved already in SDG 1, 2 \u0026amp; 8 zero hunger, no poverty, and decent work and employment opportunities for Africans.\u003c/p\u003e\u003cp\u003eImplication of Climate-Triggered GHGs on Cropland\u003c/p\u003e\u003cp\u003eThere is a need for evidence-based environmental policies to enhance feasible smart agriculture practices to encourage farmers to reduce greenhouse gas (GHG) emissions as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The real situation on ground across the savannah belt and grassland region of the SSA nations is deteriorating. This situation posed an ecological imbalance, with fast annihilation natural resources in the continent. It was identified that most of the fertile soil constituting between 58–80% of agricultural land is neither cultivated nor tilled for other use, as the lands lay in waste (uncultivated, used for economic benefit) due to inability to manage climate crisis in the Saharan region of this continent. It unveiled further, that the countries under SSA which faces numerous risks due to severe variability from climate can only depend on rain-fed agriculture as the cropland are not sustainably manage enough to yield maximum output, this assertion is also supported by an existing similar case study in the region (UNEP-UN Envoromental program 2023). The vulnerable countries are in north central Tier 1, 2, and 3 which are Ivory Coast, Togo, and Nigeria, respectively. In addition, some parts of SSA located in Eastern Africa, namely, Somalia and Uganda, and the southern part it’s just South Africa are all included. Unfortunately, space surveillances show that the majority (58%) of massive land still lies uncultivated due to its loss to climate catastrophe, rendering its values between 0–40% usefulness for food production.\u003c/p\u003e\u003ch2\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e)\u003c/h2\u003e\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cp\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe nexus between unemployment caused by harsh climatic conditions and its triggers of GHG emissions is a crucial challenge, not just to the food sector but to safe surroundings. This deters the mode and systematic approach to crop management, production and processing proves detrimental to future food and poses an environmental threat through its continued emission of GHGs and the region currently runs short of capable technological response hence dependency on rainfed agriculture is inevitable. These and other related constraint factors have compounded the low turnout of the soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e8\u003c/span\u003e), slow production circle and raise the burden of the high cost of production as peasant farmers cannot afford to irrigate their farms. Studies in the continental in comparison with agriculture-revolutionizing nations prove Irrigation practice across the world is pertinent for a successful food transformation all year round to attain food security and an overall economy boost (Bashir and Kyung-Sook 2018). Recent discovery shows that the majority (89%) of nationally determined contribution (NDC) comes from developing countries light SSA nations as the result of unsustainable agricultural operations, the more devastating issue is, that only very few NDC emitters quantified actions to reduce GHG (FAO \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, the insufficient farm settlement energy supply makes it almost impossible to automate farming operations and enhance both on-farm and off-farm management efficiency. The situation lingers to unstable employment status (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e) according to the study with evidence of high unemployment within countries like Nigeria which seem to have the highest quota of employment slots annually from agricultural-related jobs. Nigeria faced a decline in the volume of opportunities created through agricultural ventures in 1989–2003 and from 2005–2011 as well as 2014–2019 Afterward, it gained its peak employment record before declining which could be due to the upsurge of Covid. 19, South Africa is one country that has tried to remain stable in its agricultural employability plan but faces constraints due to unseasonal climate variability as a result this has resulted in seasonal lay-off workers to meet up with viable agribusiness. The same case applies to Cameroon, Kenya, Rwanda and Gambia. This result amplifies why UN SDG 8 decent work and economic attaining have been difficult couples of progressive population growth in Africa. An investigation by the World Bank (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and FOA (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003ea) shows SSA’s agriculture depends heavily on climatic conditions in the region, with the majority of employment opportunities anchored on agriculture for overall economic prospects. Seasonal variability of rainfall patterns, rising temperatures, floods, and droughts thereby causing a great challenge on food security through decreasing crop yields, animal losses, and rising food prices. It was pertinent to note that the causative factor that led to a drop in cropland productivity is higher temperature leading to an acceleration of the phenological cycle. The situation raised the critical question of the uncertainty of future food in the region as agriculture faces environmental threats caused by climate change, intensified by GHG. The recent report (COP 28 \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) United Nations Framework Convention on Climate Change (UNFCCC) presented a pressing issue for agriculture, necessitating strategies like carbon credits to mitigate emissions and enhance productivity. One of the joint signatories proposes a solution is carbon credits, a way forward for reducing emissions.\u003c/p\u003e\u003cp\u003eSustainable Approach to combat climate change \u0026amp; Actionable Step towards safeguarding future food in SSA\u003c/p\u003e\u003cp\u003eTo combat the impact of climatic action on cropland SSA, the study explores all available response options suitable and has a proven track record to suit African soil. This will be deplored in a holistic approach with local implementor, involving all stakeholders at local, national, and international corridors.\u003c/p\u003e\u003ch2\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/h2\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSustainable pathway to Combat Climate change in SSA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitigation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod of application\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffectiveness \u0026amp; Efficiency of the measure\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSustainability of adoption in the region(s)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAuthor(s)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Promoting agroforestry\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA proven study shows it slows down and, in most cases, reverses cropland degradation. It also sequesters atmospheric carbon while securing ecological services for rural livelihoods. Agroforestry is the most resilient viable nature-based approach available to SSA.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgroforestry will continue to play a vital role in a landscape-scale mitigation scheme to attain SDG 13, 1, 2 and 8 if fully harnessed in SSA. This strategy is eco-friendly and supports nature restoration. It also helps to improve soil nutrients.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Vermeulen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Incorporate climate-smart farming\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmers can use climate forecasting tools.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIt provides accurate and reliable information about climate to save farmers from production catastrophes. This situation is adverted by taking a warning from the gadget which includes the following parameters: earliest planting final planting and prospective sales.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContinue to play a role in risk management adaptation helping farmers reduce climate-related production vulnerability.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Durlacher \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Building resilience across cropland landscapes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIt has provided mutual benefits for climate change and mitigation through enhancing soil.\u003c/p\u003e \u003cp\u003eCarbon sequestration. This mitigation strategy is also active while preventing cropland erosion and runoff.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIt resuscitates and improves forest vigour while limiting wildfire. Also, it plays a great role in carbon sequestration toward sustainable ecological services.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(USDA \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Raising awareness and training farmers about climate-smart adaptation strategies\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducating, training and empowering the public on the method of its application.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis has been used to promote the adoption and application of climate-smart adaptation strategies in the United States of America under the United States Department of Agriculture (USDA) and it has proven track of success in replicated regions around the world.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited Nations FAO under USAD, NGO pledges to continue to support countries that effectively adopt and implement smart climate strategies.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Durlacher \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; USDA \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Develop climate data access points for all SSA regional farming centres.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBy gathering accurate\u003c/p\u003e \u003cp\u003escientific information on climate\u003c/p\u003e \u003cp\u003echange.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTimeline translating the data into user-friendly information.\u003c/p\u003e \u003cp\u003eFor decisions to be effectively deployed by environmental policymakers as tools, and models. This makes climate data available and accessible to all climate stakeholders.\u003c/p\u003e \u003cp\u003eIt also will help to buffer forest health by checking out carbon stock change thereby enhancing health and productivity.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIt will be easy for Forest Inventory and Analysis (FIA) to ensure forest data is kept, to facilitate the monitoring and possible sustainable long-term prediction.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(World Bank \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Increase investment and joint research support for the development of climate-smart practices and suitable technologies in the region.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChampioning the coordination, of research agencies' work relating to climate innovational discoveries. Example\u003c/p\u003e \u003cp\u003eZambia has been recommended to focus more on diversification, commercial horticulture, and agroforestry.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe NGO, intergovernmental, and regional government synergy fund will go a long way to bring in a quiet response to climate change in SSA. The climate project is not an individual task it requires collective efforts to achieve the climatic agenda of UN SDG 13 toward the set deadline.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContinue evaluating the efficacy of adoption with global barometers which provide an opportunity for climatologists in SSA to broaden their knowledge in the universal context of soil carbon storage and GHG emission reduction research being prioritized.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(UNFCCC n.d.; World Bank \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe presented solution in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e will beacon individuals, corporate bodies and government agencies with see green light. This is because the climatic crisis is a complex and multidimensional ecological challenge. Hence, it is necessary for an integrated and comprehensive approach to tackle, therefore the study proposed the above sustainable approach upon reviewing notable climatic, and environmental ecological scientist feasible findings suitable to the regional relief and economic reality. Among this local farmers' inclusiveness in planning and implementation UN SDG 13 framework and international treaties like the Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC) 2015 with a years’ cycle (Roelfsema et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The adaptation actions will help African climate stakeholders invest more in climate-safer projects and invent new technologies suitable for the region to mitigate the adverse effects of climate. These actions aim to close the gap between innovative science and technology for climate adaptation with local and international approaches. This report also canvases increased access to relevant climate data in the region and, more importantly training and educating the users on how to interpolate and deploy it concerning the intended purpose of de-carbonization and reduction of GHG towards a green economy (World Bank \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The overall aim of the mitigation includes; building resilience across cropland through aids and investments in soil, boosting public confidence in the application of climate-smart adaptation through grass root education, strengthening access points for climate data as well as its timeline availability to the end users, and lastly it has been reported that the intensification of support for research and development is crucial to scale climate-smart practices adaption in SSA.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study the greenhouse gas and it equivalent emitting index from cropland among SSA and some develop nations were investigated and the result were recorded in Kg between 1988\u0026ndash;2020. It was reported that South Africa was the highest embittered values as follows 64kg and 58kg in the year 1990 and 2017 respectively, the country recorded it lowest GHG value within this period in 2016 with 40kg value. Ethiopia was identified as the second highest emitter, in 2007 it recorded 24kg value of CO\u003csub\u003e2\u003c/sub\u003eeq.\u0026nbsp;Third is Nigeria with a mean contributory value of 21kg CO\u003csub\u003e2\u003c/sub\u003eeq.\u0026nbsp;It was uncovered through the remote sensing (RS) surveillance that the majority of about (58%) of massive land still lies uncultivated due to its loss to climate catastrophe rendering its values between 0\u0026ndash;40% usefulness for food production.\u003c/p\u003e \u003cp\u003eThe study underlined the most vulnerable part of SSA categorizing them into 3 tiers. It was also quantified the total mass area affected and the countries it lies. They include an estimated total cropland of 10881657.5 square hectares in North central Tier 2 (Nigeria, Niger) and North central Tier 3 (Sudan, Ethiopia) these region were mapped nations likely to be confronted with environmental crisis from climate variability in future.\u003c/p\u003e \u003cp\u003eOn the effect of the emission index on crops yields, it was reported that it led to intensification of unsustainable crop production, as shown in and low production of dominant staple crops such as cowpea, bean, sorghum, and ground, among others. It was forecasted of 21% further losses of ecological resources in future due to agricultural related ecological services losses.\u003c/p\u003e \u003cp\u003eThe environmental impact was identified which include ecological loss of vital natural resources and fast land degradation is continuing environmental deterioration, food insecurity, and adverse effects of climate change and its variability menace which are been witnessed across the Horn of Africa. The situation has gravitated to causing majority of SSA countries in impeding danger of climate destruction, water risk, and ecological resources diminishing annually from 2013\u0026ndash;2022 investigated.\u003c/p\u003e \u003cp\u003eThe economic impact has been witnessed in some nations like Nigeria, South Africa, Kenya, Mali and Burundi, Zambia with an outnumbered population unemployed due to significant agricultural-related job loss annually as the situation persists. The study recommends an integrated approach that will involve local farmers, extension officers, and specialists who understand the geographical feature to support all through the planning implementation stages of the response measures highlighted which include, building resilience across and cropland landscapes, use of climate-smart farming, promoting agroforestry, raising awareness and training farmers about climate-smart adaptation strategies and developing climate data access points for all SSA regional farming centres.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Responsibilities of Authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author has read, understood, and complied as applicable with the statement on \u0026quot;Ethical responsibilities of Authors\u0026quot; as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere is no fund or grant, or any financial support received in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll relevant data are within the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial interests\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmmanuel Igwe Conceptualization, Methodology, Investigation, Software (RS and GIS) application, Writing\u0026ndash;Original draft preparation, review and edit the final draft\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe author declares no conflict of interest There are no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: \u0026nbsp;This work was overseen by my academic supervisor the head postgraduate program at Ecology Institute RUDN University Prof. Daria O. Kapralova who supported me in interpolating the data generated from remote sensing and geographical information system (GIS) as a specialist. Also, the study received special revision support from Dr. \u0026nbsp;Valerien Baharane from the Department of Environmental Safety and Product Quality Management at RUDN University.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkon, M., Com, H., \u0026amp; Daly, R. (2011). ON THE ON THE. \u003cem\u003eOrder A Journal On The Theory Of Ordered Sets And Its Applications\u003c/em\u003e, 1\u0026ndash;2. https://doi.org/10.7930/NCA4.2018.CH13\u003c/li\u003e\n\u003cli\u003eAlewell, C., Borrelli, P., Meusburger, K., \u0026amp; Panagos, P. (2019). 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Maximum Nighttime Urban Heat Island (UHI) Intensity Simulation by Integrating Remotely Sensed Data and Meteorological Observations. \u003cem\u003eIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 138\u0026ndash;146. https://doi.org/10.1109/JSTARS.2010.2070871\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"GHGs, anthropogenesis, climate change, remote sensing, climate-smart technologies, LULC","lastPublishedDoi":"10.21203/rs.3.rs-5261257/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5261257/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCroplands are one of the world's leading single major contributors to global greenhouse gas (GHG) emissions with more than 20% share of the emitted GHG, at the same time depending on the climate to produce its yields, this situation is significantly felt in Sub-Saharan Africa (SSA) due to the unavailability of mitigating technologies. Satellite image of sentinel-7 was deployed to capture real-time virtual images of land use land cover (LULC) showing a proportion (58%) of massive agricultural land in the region still lies uncultivated due to its losses to climate catastrophe that endangers and rendered between 0\u0026ndash;40% usefulness for food production valueless. This study deploys various measuring metrics to examine the intensity of climate variability using panel data, as well as real-time data from remote sensing (RS) to verify and make a comparison of CO\u003csub\u003e2\u003c/sub\u003eeqKg emitting capacity from leading croplands major countries in SSA. Estimate stochastic frontier analysis (SFA) was used to compute and assemble from 1988 to 2022. The result revealed within the six closely monitored countries their emitting rate with South Africa led as the highest emitter of CO\u003csub\u003e2\u003c/sub\u003e equivalent in kg in these years, with its peak annually recorded in 1990 at an estimated value of 64kg CO2eqKg followed by 2017 with a value of about 58 kg CO\u003csub\u003e2\u003c/sub\u003eeqKg while Ethiopia came second with its second-highest emitting rate in 2007 with a value of 24kg CO\u003csub\u003e2\u003c/sub\u003eeqKg followed by Nigeria with mean contributory value of 21Kg CO\u003csub\u003e2\u003c/sub\u003eeqKg. It unveiled an estimated total cropland of 10881657.5 square hectares in North central Tier 2 (Nigeria, Niger) and North central Tier 3 (Sudan, Ethiopia) as the hotspot of the GHG emission index. The study further presented 2013\u0026ndash;2022 as the most diminution years in the region with a forecasted 21% ecological resources (aquatic species) decline in the coming year with a burden of more disastrous ecological resources in most likely affected nations such as Nigeria, South Africa, Kenya, Mali, and Burundi, Zambia as they are mapped as the most vulnerable to these unforeseen longtime environmental consequences. The study suggests adopting locally developed innovative technologies compatible with current climate resilience strategies, to be implemented through a comprehensive approach.\u003c/p\u003e","manuscriptTitle":"The Assessment of Sub-Saharan Africa's GHG emission from cropland in comparison to some developing nations, its environmental economic impacts, and mitigation measures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 07:32:49","doi":"10.21203/rs.3.rs-5261257/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-24T23:33:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-23T02:23:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-23T02:23:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-10-14T12:49:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9de818a-a64e-45d9-a366-c6a9b3298b65","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:01:04+00:00","versionOfRecord":{"articleIdentity":"rs-5261257","link":"https://doi.org/10.1007/s10661-025-13633-2","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2025-01-30 15:57:20","publishedOnDateReadable":"January 30th, 2025"},"versionCreatedAt":"2024-11-20 07:32:49","video":"","vorDoi":"10.1007/s10661-025-13633-2","vorDoiUrl":"https://doi.org/10.1007/s10661-025-13633-2","workflowStages":[]},"version":"v1","identity":"rs-5261257","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5261257","identity":"rs-5261257","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}