Citizen Science and Remote Sensing for Crop Yield Gap Analysis Eskender Andualem Beza 2017 Beza Cover.Indd Alle Pagina's

Citizen science and remote sensing for crop yield gap analysis Eskender Andualem Beza Citizen science and remote sensing for crop yield gap analysis Eskender INVITATION It is my pleasure to invite you to attend the public defense of my PhD thesis entitled Citizen science and remote sensing for crop yield gap analysis On Tuesday August 29, 2017 at 4:00 p.m. in the Aula of Wageningen University, Generaal Foulkesweg 1, Wageningen Eskender Beza [email protected] The ceremony will be followed by a reception Paranymphs: Saba Eshetie [email protected] Kalkidan Mulatu [email protected] 2017 Beza_cover.indd Alle pagina's 24-7-2017 16:57:26 Citizen science and remote sensing for crop yield gap analysis Eskender Andualem Beza Thesis committee Promotor Prof. Dr M. Herold Professor of Geo-information Science and Remote Sensing Wageningen University & Research Co-promotors Dr L. Kooistra Associate professor, Laboratory of Geo-information Science and Remote Sensing Wageningen University & Research Dr P. Reidsma Assistant professor, Plant Production Systems Group Wageningen University & Research Other members Prof. Dr C. Leeuwis, Wageningen University & Research Dr C.A.J.M. de Bie, University of Twente, Enschede Dr K. Tesfaye Fantaye, International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia Prof. Dr F. Ludwig, Wageningen University & Research This research was conducted under the auspices of the C.T. de Wit Graduate School of Production Ecology & Resource Conservation (PE&RC) Citizen science and remote sensing for crop yield gap analysis Eskender Andualem Beza Thesis submitted in fulfilment of the requirements for the degree of doctor at Wageningen University by the authority of the Rector Magnificus, Prof. Dr A.P.J. Mol, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Tuesday 29 August 2017 at 4 p.m. in the Aula. Eskender Andualem Beza Citizen science and remote sensing for crop yield gap analysis, 204 pages. PhD thesis, Wageningen University, Wageningen, the Netherlands (2017) With references, with summary in English and Dutch ISBN: 978-94-6343-641-0 DOI: 10.18174/420049 Table of Contents Page Chapter 1 Introduction 1 Chapter 2 Review of yield gap explaining factors and 13 opportunities for alternative data collection approaches Chapter 3 What are the prospects for citizen science in 49 agriculture? Evidence from three continents on motivation and mobile telephone use of resource- poor farmers Chapter 4 Exploring farmers’ intentions to adopt mobile 83 Short Message Service (SMS) for citizen science in agriculture Chapter 5 Remote sensing and crowdsourcing for estimating 119 and explaining yields: sesame in Ethiopia Chapter 6 Synthesis 143 References 159 Summary (English) 183 Summary (Dutch) 187 Acknowledgments 191 List of publications 193 About the author 194 PE&RC Training and Education Statement 195 Chapter 1 1 Introduction Chapter 1 1.1 Background The world population is expected to peak around 9 billion by the middle of this century (Bruinsma 2009) and the question how to feed this large number of people will be one of the greatest challenges facing humanity in the coming decades (van Ittersum et al. 2016; Godfray et al. 2010). Options to bridge the gap between projected food demand and current level of supply include: 1) expansion of land under cultivation, 2) intensification on currently available farmlands by growing two or three crops per year, 3) reducing the yield gap in farmers’ fields, 4) raising the yield ceiling by introducing higher yielding varieties, and 5) reducing postharvest losses and food waste (Keating et al. 2014; Godfray et al. 2010; Koning and van Ittersum 2009). Ramankutty et al. (2008) showed that roughly half of the land that is suitable for agriculture on the planet is already under cultivation today. They also showed that much of the remaining cultivable land covers the tropical rain forests of South America and Africa; biomes that have high economic, social and ecological values. Therefore, the option of expansion of land under cultivation has associated costs such as deforestation, biodiversity loss and, soil degradation. The other alternative and promising strategy to increase agricultural production is to intensify production. While yield ceilings (potential yields) (Hall and Richards 2013) and cropping intensity may be further increased, the largest role is foreseen for reducing the gap between yields currently achieved on farmers’ fields and those that can be achieved by using the best adapted crop varieties with the best current crop and land management practices for a given environment (Keating et al. 2014; van Ittersum et al. 2013). In order to estimate the magnitude of the yield gap, two quantities are required: the benchmarking yield and the actual yield. For the benchmark yield two situations can be distinguished: yield potential or potential yield (Yp) for irrigated crops or water-limited yield potential (Yw) for rainfed crops. Yp is the maximum theoretical yield achieved by a specific crop genotype in a well-defined biophysical environment, with both abiotic (water and nutrients) and biotic factors (pests, diseases and weeds) effectively controlled (van Ittersum and Rabbinge 1997). Yp is determined by solar radiation, temperature, atmospheric CO2 and crop characteristics that govern the length of the growing period (cultivar or hybrid maturity) and light interception by the crop canopy (i.e., canopy structure) (van Ittersum et al. 2013). For rainfed crops, water-limited yield potential, also known as water- limited (potential) yield, is the most relevant benchmarking yield (van Ittersum et al. 2013). The definition of Yw is similar to Yp, but in the case of Yw, crop growth is also limited by water supply. The actual yield (Ya) is defined as the yield currently realised in farmers’ fields. To capture variation in space and time for a specific location, van Ittersum et al. (2013) defined actual yield as 2 Introduction the spatial and temporal average yield achieved by farmers in the region for the most widely used management practices (sowing date, cultivar maturity, and plant density, nutrient management and crop protection). The yield gap (Yg) is then defined as the difference between Yp (for irrigated crops), or Yw (for rainfed crops) and the actual yield (Ya). Figure 1.1 illustrates the schematic representation of the yield gap concept. Yield gap analysis is critical for four main reasons (van Ittersum et al. 2013). First, yield gap analysis provides the foundation to identify the main crop, soil and management factors limiting current farm yields and improved practices to close the yield gap (Lobell et al. 2009). Second, to enable effective prioritization of research, development and interventions (van Oort et al. 2017). The third reason is to evaluate the impact of climate change and other future scenarios that influence land and natural resource use (Reidsma et al. 2010). And fourth, results from yield gap analysis are important inputs to economic models that assess food security and land use at different spatial scales. In yield gap analysis, two major steps can be identified. The first step is to quantify the magnitude and distribution of the yield gap. The second step is to identify the main factors that cause the yield gap (Lobell 2013). A prominent example of the first step is the Global Yield Gap Atlas (GYGA), a project designed to quantify the size and distribution of the yield gap for the major crops across the globe (www.yieldgap.org) (Hochman et al. 2016). In the GYGA project, a “bottom-up” approach for yield gap analysis has been proposed that can be applied globally, but has a strong local agronomic relevance and exploits local knowledge and data (van Ittersum et al. 2013). The GYGA “bottom-up” approach recommends (1) the use of well-calibrated crop growth simulation models applied to relatively homogenous climate zones, (2) the use of measured daily weather data, (3) the simulations to estimate Yp or Yw to be done for the dominant soil types and cropping systems, taking into consideration the current spatial crop distribution, (4) the use of site-specific agronomic and actual yield information, (5) empirical verification at the local level of estimated yield gaps with on-farm data and experiments and (6) the use of an explicit methodology for up-scaling (www.yieldgap.org) (Boogaard et al. 2013). At the time of writing, the yield gaps of 9 field crops in 47 countries (one up to five crops per country) have been estimated using the aforementioned protocols and work is underway in another five countries (www.yieldgap.org; last accessed March 10, 2017). 3 Chapter 1 CO2 Radiation Potential (Yp) Defining factors Temperature Cultivar features Water-limited (Yw) Limiting factor Water Water & nutrient-limited Limiting factors Water Nutrients Production situation Weeds Actual (Ya) Reducing factors Pests and diseases Pollutants Production level (Mg/ha) Figure 1.1 Different production levels as determined by growth defining, limiting and reducing factors (van Ittersum et al. 2013). Several studies have examined the yield gap at the regional or global scale using aggregated data for actual crop yield and explaining factors (Mueller et al. 2012; Foley et al. 2011; Neumann et al. 2010; Licker et al. 2010). These types of “top-down” studies are useful in quantifying the scope for yield improvement in a broad sense and comparing different regions using harmonised data (van Ittersum et al. 2013). Moreover, such global studies are useful to understand the spatial variability of yield gaps at a larger scale, along with trends across regions (Mueller et al. 2012; Neumann et al. 2010). However, there are a number of issues associated with such “top-down” yield gap studies (van Ittersum et al. 2013). For example, these studies mostly use global datasets on agricultural management (e.g., Waha et al. 2012) and actual yields (Monfreda et al.

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