As pressure on agricultural land increases, there is an urgent need to develop easy-to-use tools, that are applicable at a wide range of geographic scales, to improve our understanding of the trade-offs between food production and the protection of ecosystems

Adolph, Barbara

Davies, Jo

Devenish, Adam

Franks, Phillip

Griffiths, Geoff

Jellason, Nugun P.

Kwenye, Jane M.

Manzoor, Syed Amir

Robinson, Elizabeth

Zurek, Monica

Central Archive at the University of Reading

Funded by:Partners:SummaryScenario analysis allows policymakers to explore and map plausible futures in a systematic manner; yet there are few examples of applying this tool at local scales, thereby missing the detail needed for a better understanding of local processes. In collaboration with local farmers, serious gaming is used to map the impacts of future land cover change on biodiversity and ecosystems in Zambia.BackgroundAs global populations grow and demand for agricultural land increases, conflicts between competing land uses need to be minimised. Tools are being developed to aid decision makers, such as a land change modelling (LCM) technique described in this brief, to project land cover change at the local scale in in Zambia to 2050, based on a set of scenarios that emerged from an exercise in serious gaming with local farmers. Serious gaming to inform local scale scenarios of land cover change to 2050 in North-Western province, ZambiaAs pressure on agricultural land increases, there is an urgent need to develop easy-to-use tools, that are applicable at a wide range of geographic scales, to improve our understanding of the trade-offs between food production and the protection of ecosystems. Cereal production in Zambia tripled over the period 1994–2014 and food demand in Africa is projected to again triple over the period 2010–2050.1 Recent work has demonstrated the potential utility of modelling and mapping future land cover change at national scales (in Zambia, Ghana and Ethiopia) as part of the Social and Environmental Trade-Offs in African Agriculture (Sentinel) project.2 This provided useful insights into how national policy can influence the critical trade-offs between agricultural expansion and the protection of biodiversity and other ecosystem services, but failed to provide the level of detail that is essential for land-use planning at sub-national scales (district and below).3 The national-scale scenarios were developed by national-level experts drawn from government, civil society, academia and NGOs during a series of workshops,4 but without representation from farmers who make day-to-day decisions about future land use. Furthermore, it was inevitable that at the national scale, the physical (soils, topography, and climate) and cultural (farming system, land tenure, etc) conditions were assumed to be uniform and that national policies could be applied equally across all regions. This made a strong case to undertake land change modelling LCM at sub-national scales, either within separate ecoregions5 or at the provincial/district level, to account for the diversity of physical and cultural landscapes across the country. Scenario analysis allows policymakers to explore and map plausible futures in a systematic manner.6 A novel technique – serious gaming – was used to explore farmers’ decision-making processes when determining whether or not to expand into previously uncultivated forest land.7 Survey data and community consultation were used to inform the game design. Farmers were assigned into teams of two, coded by colour (red farm, blue farm, etc). The starting conditions (amount of land, soil fertility, wealth, access to labour) were different for each team, representing a variety of wealth categories. This technique proved useful for identifying and evaluating the consequences of alternative actions and was an effective approach in uncovering the key factors that motivated expansion Funded by:Partners:Policy Brief October 2022 Globe www.sentinel-gcrf.org into forest and other native vegetation. The results were used to develop four contrasting scenarios which were subsequently modelled using LCM and visualised as maps of projected land cover change to 2050. ScenariosThe serious gaming exercise was played with six farms in different wealth categories (better off, 25%; intermediate, 23%; poor, 52%). Unlike at national level where the scenarios that emerged from the stakeholder workshop were based on climate change and governance, at the local scale differences in soil fertility and crop prices were the most important factors influencing future land use decisions by farmers (Table 1). Table 1: The significant drivers of land cover change for four scenarios at the local scale, Chitokoloki, North-Western province, ZambiaDriver one: Impact of soil fertilityLow HighDriver two:  Crop prices HighScenario 1: Scenario 2:LowScenario 4: Scenario 3:The gaming exercise was followed with a debrief in which participants discussed the factors that motivated them to expand into surrounding forests, some of which are quoted below: Black farm (male): “Yes, we will expand into the forest because high prices mean more profits for us”.Blue farm (female): “Expanding into the forest will be the order of the day because our soils are poor here in Chitokoloki. Therefore, in order to increase crop yield given the increase in price farmers will be motivated to expand into the forest where soils are more fertile”.Red farm (female): “Our crops are very poor in Chotokoloki. This is what causes us to go into the forest where soils are more fertile. So, if provision of inputs is enhanced then farmers can stay on their fields and not clear the forest because the inputs can improve crop yield”. Green farm (male): “Encouraging crop rotation can also help because it can help to improve our soils”. Red farm (male): “Introducing crops that help to improve soil fertility can also help to encourage farmers to stay on their fields. Farmers expand into the forest in search of fertile soils. Some crop like soya beans should be introduced in this community”.Based on the outcome of the serious gaming, the following general assumptions were made in guiding the interpretation of areas for land demand: • Soil fertility drives decisions about agricultural production, specifically whether to expand into forest, intensify, or diversity• Crop price intersects with soil fertility and the availability of inputs (for example, farmer input support programme (FISP ) designed to pay for improved crop varieties, fertilise etc) to determine whether a farmer expands into forest, intensifies or diversifies. Based on the serious gaming exercise, the following scenarios were developed and described: Scenario 1: High crop prices and maintenance of good soil fertility result in good profits and spare cash to expand, leading to a significant loss of closed/open woodland and other native vegetation, including grass/shrub.Scenario 2: High crop prices and declining soil fertility result in strong motive to intensify, using extra cash from crop sales to purchase inputs.Scenario 3: Low crop prices combined with declining soil fertility result in rapid expansion into areas of closed open forest and other native vegetation.Scenario 4: Maintenance of soil fertility but low crop prices provide the motivation to diversify, planting different crops to minimise the risk of poor harvests resulting from poor fertility. This scenario is nearly equivalent, therefore, to B-a-U.Business-as-Usual (B-a-U): Business-as-Usual is a simple linear extrapolation of past trends in land cover change (2000–2021) derived from the most recent good quality land cover data available from satellite imagery. It assumes that the factors affecting land cover change remain stable and strictly, is not a scenario. Method: transforming qualitative storylines into quantitative change in land cover The process of translating the description of each scenario into a table of land ‘demand’ relies on a qualitative assessment of the area of land required to satisfy the conditions of each of the four scenarios for each land cover category at an arbitrary year in the future, in this case 2050. The direction and magnitude of change for each scenario were used to estimate the ‘land demand’ for each land cover type Sentinel 2Serious gaming to inform local scale scenarios of land cover change to 2050under B-a-U and the four scenarios (Table 2). For example, there is a strong contrast between Scenario 2 (S2) and Scenario 3 (S3). For S2, despite declining soil fertility, high crop prices are driving intensification with the use of inputs (fertiliser, improved crop varieties etc), resulting overall in relatively stable land use. By contrast, for S3, low soil fertility is not offset by high crop prices resulting in a strong motivation to expand into forest areas, leading to rapid land use change.Terrset Land Change Modeller (LCM) software was used to project future land cover change to 2050.8 The technique relies on ‘explaining’ the observed change in land cover mapped from two sets of classified satellite images9,10 with reference to a set of statistically significant physical (soils, topography) and socio-economic (population, proximity to roads/settlements) variables. These significant variables are subsequently used to generate maps of land cover to 2050 based on the land demand for each land cover category generated for each of the four scenarios. Table 2 illustrates the considerable increase in cropland (5.5%) and the decline of closed woodland (more than 13%) between 2000 and 2021, and how the trajectories of projected change to 2050 for the four land cover types vary between scenarios.ResultsThe results are mapped as land cover transitions, showing the type and location of land cover change to 2050 for B-a-U and the four scenarios, although only selected examples are shown here. Figure 1, for example, shows the difference in the projected expansion of cropland between 2021 and 2050 for S2 and S3. Whilst some of this expansion is along water courses, roads and close to existing settlements, the expansion is generally widespread across the study area and appears to be at the expense of closed woodland. This is confirmed in Figure 2, which shows how the projected loss of closed woodland between 2021 to 2050 corresponds closely to the distribution of cropland expansion in Figure 1. Serious gaming to inform local scale scenarios of land cover change to 2050Sentinel 3Table 2: Land cover demand (low, medium, high) under the four scenarios and Business-as-Usual    % change 2021–2050  Land cover: 2000Land cover: 2021B-a-U Scenario 1 Scenario 2 Scenario 3 Scenario 4Cropland  6.65 12.2      Closed woodland32.83 19.4      Open woodland12.53 20.0      Other (riparian, grass/shrub) 47.98 49.8        Positive Negative       Low 1–20 Low 1–20       Medium 20–40 Medium 20 –40       High > 40 High > 40            The colours are designed to show positive (green shades) and negative (red shades) as low, medium and high change between the 2021 baseline and 2050Serious gaming to inform local scale scenarios of land cover change to 2050Sentinel 4Figure 1: The difference between Scenarios 2 and 3, showing the projected expansion of cropland to 2050Figure 2: The difference between Scenarios 2 and 3, showing the projected loss of closed woodland to 2050,  mostly to cropland expansion (Figure 1)Sentinel is an interdisciplinary research project seeking to address the challenge of achieving ‘zero hunger’ in sub-Saharan Africa, while at the same time reducing inequalities and conserving ecosystems.Arrow-Alt-Circle-Down Download this report at  www.sentinel-gcrf.org/publicationsGlobe www.sentinel-gcrf.org AuthorsGeoffrey Griffiths, School of Agriculture, Policy and Development, University of Reading, UKSyed Amir Manzoor, University of Multan, PakistanBarbara Adolph, Principal Researcher, International Institute for Environment and Development, LondonJo Davies, School of Agriculture, Policy and Development, University of Reading, UKAdam Devenish, Imperial Collage, LondonPhilip Franks, Principal Researcher, International Institute for Environment and Development, LondonNugun P Jellason, School of Agriculture, Policy and Development, University of Reading, UKJane M Kwenye, School of Natural Resources, The Copperbelt University Elizabeth Robinson, London School of Economics, UKMonika Zurek, Environmental Change Institute, The University of Oxford, UK Corresponding author:  Geoffrey Griffiths, g.h.griffiths@reading.ac.ukAcknowledgementsWe kindly acknowledge the QGIS project for open-source GIS tools; the farmers of a community in North-Western province who participated in the serious gaming exercise; and members of the field team.Image credits:  Front cover: Barbara Adolph/SentinelPublisher: Sentinel / International Institute  for Environment and DevelopmentThird Floor, 235 High Holborn, London,  WC1V 7DN, UKTel: +44 (0)20 3463 7399  Email: info@iied.orgwww.iied.orgFunding: Sentinel is funded by UK Research & Innovation (UKRI) through the Global Challenges Research Fund (GCRF) programme for ‘Growing research capability to meet the challenges faced by developing countries’ (‘GROW’). Grant Ref: ES/P011306/1. However, the contents of this document are the sole responsibility of the authors and do not necessarily reflect the position of our funders.This publication may be shared and reproduced in accordance with the Creative Commons Attribution 4.0 International licence (CC BY 4.0). Conclusion Serious gaming is a participatory tool that can be used to identify the key factors that motivate farmers’ land use decision making. However, challenges arise in ensuring that the game is calibrated to local conditions, and that decisions in the game are as accurate as possible in reflecting real-world conditions (ie the trade-off between simplifying for the game to work, while not actually losing too much of the analytical power of the game). This local scale ‘on the ground’ analysis is invaluable for developing scenarios of change that can be visualised as maps under different sets of assumptions to aid understanding of the trade-offs between agricultural production and the protection of ecosystem services. An important next step will be to demonstrate how the integration of serious gaming, scenario development, and land cover change modelling can be used for land use planning. References 1	 IFPRI,	Foresight	modeling	with	IFPRI’S	Impact	Model.	www.ifpri.org/program/impact-model 2	 Griffiths,	G,	Adolph,	B,	Chapman,	A,	Davies,	J,	Devenish,	A,	Jellason,	N,	Kanja,	K,	Kwenye,	J,	Manzoor,	A,	Mwitwa,	J,	Newbold,	T,	Phiri,	D,	Robinson,	E	and	Zurek,	M	(2022)	Future	trade-offs	to	2050: land cover change, biodiversity and agriculture in Zambia. IIED, London. Sentinel, London. https://pubs.iied.org/20896g 3	 Franks,	P	(2019)	Conservation	versus	food	production	in	Africa:	better	managing	trade-offs. IIED, London. https://pubs.iied.org/17738IIED  4	 Kwenye,	J,	Mwitwa,	J,	Zurek,	M,	Adolph,	B,	Devenish,	A,	Franks,	P,	and	Hebinch,	A	(2022)	Scenarios of agricultural development in Zambia - Produced during the Sentinel participatory scenario development workshop 2018, Sentinel / IIED, London. https://www.sentinel-gcrf.org/scenarios-agricultural-development-zambia-produced-during-sentinel-participatory-scenario 5	 WWF	(1	August	2012)	Terrestrial	Ecoregions	of	the	World.	www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world 6	 Zurek,	MB	and	Henrichs,	T	(2007)	Linking	scenarios	across	geographical	scales	in	international	environmental assessments. Technological Forecasting and Social Change 74, 1282–1295. https://doi.org/10.1016/j.techfore.2006.11.005 7	 Den	Haan,	R-J	and	Van	der	Voort,	MC	(2018)	On	Evaluating	Social	Learning	Outcomes	of	Serious	Games to Collaboratively Address Sustainability Problems: A Literature Review. Sustainability 10. https://doi.org/10.3390/su10124529 8	 Clark	Labs,	Land	Change	Modeler	in	TerrSet.	https://clarklabs.org/terrset/land-change-modeler 9	 Landsat	(2000)	USGS,	Landsat	Missions.	www.usgs.gov/landsat-missions10 Landsat (2021) USGS, Landsat Missions. www.usgs.gov/landsat-missionsSerious gaming to inform local scale scenarios of land cover change to 2050

Serious gaming to inform local scale scenarios of land cover change to 2050 in North-Western province, Zambia

https://centaur.reading.ac.uk/108274/1/SentinelBriefingZambia5.pdf

Serious gaming to inform local scale scenarios of land cover change to 2050 in North-Western province, Zambia

Abstract

Similar works

Full text

Available Versions

Central Archive at the University of Reading