Citizen science and remote sensing for crop yield gap analysis
Beza, Eskender Andualem - \ 2017
Wageningen University. Promotor(en): M. Herold, co-promotor(en): L. Kooistra; P. Reidsma. - Wageningen : Wageningen University - ISBN 9789463436410 - 196
crop yield - maximum yield - yield forecasting - remote sensing - models - small farms - data collection - gewasopbrengst - maximum opbrengst - oogstvoorspelling - remote sensing - modellen - kleine landbouwbedrijven - gegevens verzamelen
The world population is anticipated to be around 9.1 billion in 2050 and the challenge is how to feed this huge number of people without affecting natural ecosystems. Different approaches have been proposed and closing the ‘yield gap’ on currently available agricultural lands is one of them. The concept of ‘yield gap’ is based on production ecological principles and can be estimated as the difference between a benchmark (e.g. climatic potential or water-limited yield) and the actual yield. Yield gap analysis can be performed at different scales: from field to global level. Of particular importance is estimating the yield gap and revealing the underlying explanatory factors contributing to it. As decisions are made by farmers, farm level yield gap analysis specifically contributes to better understanding, and provides entry points to increased production levels in specific farming systems. A major challenge for this type of analysis is the high data standards required which typically refer to (a) large sample size, (b) fine resolution and (c) great level of detail. Clearly, obtaining information about biophysical characteristics and crop and farm management for individual agricultural activities within a farm, as well as farm and farmer’s characteristics and socio-economic conditions for a large number of farms is costly and time-consuming. Nowadays, the proliferation of different types of mobile phones (e.g., smartphones) equipped with sensors (e.g., GPS, camera) makes it possible to implement effective and low-cost “bottom-up” data collection approaches such as citizen science. Using these innovative methodologies facilitate the collection of relatively large amounts of information directly from local communities. Moreover, other data collection methods such as remote sensing can provide data (e.g., on actual crop yield) for yield gap analysis.
The main objective of this thesis, therefore, was to investigate the applicability of innovative data collection approaches such as crowdsourcing and remote sensing to support the assessment and monitoring of crop yield gaps. To address the main objective, the following research questions were formulated: 1) What are the main factors causing the yield gaps at the global, regional and crop level? 2) How could data for yield gap explaining factors be collected with innovative “bottom-up” approaches? 3) What are motivations of farmers to participate in agricultural citizen science? 4) What determines smallholder farmers to use technologies (e.g., mobile SMS) for agricultural data collection? 5) How can synergy of crowdsourced data and remote sensing improve the estimation and explanation of yield variability?
Chapter 2 assesses data availability and data collection approaches for yield gap analysis and provides a summary of yield gap explaining factors at the global, regional and crop level, identified by previous studies. For this purpose, a review of yield gap studies (50 agronomic-based peer-reviewed articles) was performed to identify the most commonly considered and explaining factors of the yield gap. Using the review, we show that management and edaphic factors are more often considered to explain the yield gap compared to farm(er) characteristics and socio-economic factors. However, when considered, both farm(er) characteristics and socio-economic factors often explain the yield gap. Furthermore, within group comparison shows that fertilization and soil fertility factors are the most often considered management and edaphic groups. In the fertilization group, factors related to quantity (e.g., N fertilizer quantity) are more often considered compared to factors related to timing (e.g., N fertilizer timing). However, when considered, timing explained the yield gap more often. Finally, from the results at regional and crop level, it was evident that the relevance of factors depends on the location and crop, and that generalizations should not be made. Although the data included in yield gap analysis also depends on the objective, knowledge of explaining factors, and methods applied, data availability is a major limiting factor. Therefore, bottom-up data collection approaches (e.g., crowdsourcing) involving agricultural communities can provide alternatives to overcome this limitation and improve yield gap analysis.
Chapter 3 explores the motivations of farmers to participate in citizen science. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. Using the developed questionnaire, semi-structured interviews were conducted with smallholder farmers in three countries (Ethiopia, Honduras and India). The results show that for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. Moreover, the majority of the farmers in the three countries indicated that they would like to receive agronomic advice, capacity building and seed innovation as the main returns from the citizen science process. Country and education level were the two most important farmers’ characteristics that explained around 20% of the variation in farmers’ motivations. The results also show that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. For example fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest.
Chapter 4 investigates the factors that determine farmers to adopt mobile technology for agricultural data collection. To identify the factors, the unified theory of acceptance and use of technology (UTAUT2) model was employed and extended with additional constructs of trust, mastery-approach goals and personal innovativeness in information technology. As part of the research, we setup data collection platforms using open source applications (Frontline SMS and Ushahidi) and farmers provided their farm related information using SMS for two growing seasons. The sample for this research consisted of group of farmers involved in a mobile SMS experiment (n=110) and another group of farmers which was not involved in a mobile SMS experiment (n=110), in three regions of Ethiopia. The results from the structural equation modelling showed that performance expectancy, effort expectancy, price value and trust were the main factors that influence farmers to adopt mobile SMS technology for agricultural data collection. Among these factors, trust is the strongest predictor of farmer’s intention to adopt mobile SMS. This clearly indicates that in order to use the citizen science approach in the agricultural domain, establishing a trusted relationship with the smallholder farming community is crucial. Given that performance expectancy significantly predicted farmer’s behavioural intention to adopt mobile SMS, managers of agricultural citizen science projects need to ensure that using mobile SMS for agricultural data collection offers utilitarian benefits to the farmers. The importance of effort expectancy on farmer’s intention to adopt mobile SMS clearly indicates that mobile phone software developers need to develop easy to use mobile applications.
Chapter 5 demonstrates the results of synergetic use of remote sensing and crowdsourcing for estimating and explaining crop yields at the field level. Sesame production on medium and large farms in Ethiopia was used as a case study. To evaluate the added value of the crowdsourcing approach to improve the prediction of sesame yield using remote sensing, two independent models based on the relationship between vegetation indices (VIs) and farmers reported yield were developed and compared. The first model was based on VI values extracted from all available remote sensing imagery acquired during the optimum growing period (hereafter optimum growing period VI). The second model was based on VI values extracted from remote sensing imagery acquired after sowing and before harvest dates per field (hereafter phenologically adjusted VI). To select the images acquired between sowing and harvesting dates per field, farmers crowdsourced crop phenology information was used. Results showed that vegetation indices derived based on farmers crowdsourced crop phenology information had a stronger relationship with sesame yield compared to vegetation indices derived based on the optimum growing period. This implies that using crowdsourced information related to crop phenology per field used to adjust the VIs, improved the performance of the model to predict sesame yield. Crowdsourcing was further used to identify the factors causing the yield variability within a field. According to the perception of farmers, overall soil fertility was the most important factor explaining the yield variability within a field, followed by high presence of weeds.
Chapter 6 discusses the main findings of this thesis. It draws conclusions about the main research findings in each of the research questions addressed in the four main chapters. Finally, it discusses the necessary additional steps (e.g., data quality, sustainability) in a broader context that need to be considered to utilize the full potential of innovative data collection approaches for agricultural citizen science.
Uncertainty and sensitivity analysis of algae production models for flat panel photobioreactors
Stefanov, M.S. ; Slegers, P.M. ; Boxtel, A.J.B. van - \ 2012
gewasgroeimodellen - algen - algenteelt - oogstvoorspelling - fotobioreactoren - onderzoeksprojecten - biobased economy - biomassa productie - crop growth models - algae - algae culture - yield forecasting - photobioreactors - research projects - biobased economy - biomass production
Poster met onderzoeksinformatie.
Predictive modelling of large scale algae biomass production
Slegers, P.M. ; Wijffels, R.H. ; Straten, G. van; Boxtel, A.J.B. van - \ 2012
gewasgroeimodellen - algen - algenteelt - oogstvoorspelling - biomassa productie - cultuurmethoden - biobased economy - onderzoeksprojecten - crop growth models - algae - algae culture - yield forecasting - biomass production - cultural methods - biobased economy - research projects
Poster met onderzoeksinformatie.
Quantification of dynamics of soil-borne pathogens and their consequences for yield in crop rotations
Berg, W. van den - \ 2011
Wageningen University. Promotor(en): Johan Grasman, co-promotor(en): Walter Rossing. - [S.l.] : S.n. - ISBN 9789461730497 - 127
gewassen - rotaties - bodempathogenen - plantenparasitaire nematoden - populatiedynamica - oogstvoorspelling - proefopzet - modellen - dynamische modellen - statistische analyse - crops - rotations - soilborne pathogens - plant parasitic nematodes - population dynamics - yield forecasting - experimental design - models - dynamic models - statistical analysis
In The Netherlands crop rotation experiments were performed that required a continuation over many years so that long term effects of treatments and rotations may build up. Usually, analysis of variance is performed on within year data or on data from the final years when treatment/rotation effects are maximal. In this thesis the scope is on rotation experiments in the presence of soil-borne pathogens. Instead of estimating the mean densities of these pathogens and the mean yields of the crops over the whole duration of the experiment, dynamics of the pathogens and yield were registered yearly. Two types of dynamical models were applied. In the first model the dependence of the final (end of the season) pathogen density upon the initial density was represented by a monotonically rising function. The model was applied to an experiment on the dynamics of Pratylenchus penetrans in vegetable crops. In the second model with a Ricker type of functional relation between initial and final pathogen densities, the dynamics of Globodera pallida on 6 cultivars of potato was studied. In both models the pathogen density tended to a stable steady state in the long term. For this steady state an economic evaluation of crop rotations was made. In a third study potato tuber yield was predicted taking into account the presence of three nematode species and the fungus V. dahliae. Also the effect of abiotic factors such as pH and the P content of the soil were taken in consideration. Based on information theory the results of a large class of feasible models was combined by using a suitable method of averaging values of parameters of the different models. Moreover, this method also ranks the predictors with respect to their predictive power. In a fourth study, optimum designs of experiments on pathogen dynamics and yield loss were derived for one of the cultivars already tested in the second study. Furthermore, a measure for the efficiency of a design was introduced so that designs can be compared.
|Het optimale pluktijdstip is te voorspellen : rode bessen
Westra, E.H. ; Verschoor, J.A. - \ 2010
De Fruitteelt 100 (2010)19. - ISSN 0016-2302 - p. 18 - 19.
rode aalbessen - ribes rubrum - oogsttijdstip - plukken (picking) - voorspellen - oogstvoorspelling - modellen - landbouwkundig onderzoek - temperatuurmeters - red currants - harvesting date - picking - forecasting - yield forecasting - models - agricultural research - temperature gauges
Voor de lange bewaring van rode bessen moeten telers op het juiste moment plukken. Te vroeg oogsten geeft uitval door rot. Wageningen UR Food & Biobased Research (voorheen AFSG) ontwikkelde een voorspellingsmodel om het optimale pluktijdstip te kunnen bepalen op basis van de temperatuur in de periode na de bloei.
|Aan de kwaliteit ligt het niet ... : weinig afwijkingen
Geijn, F.G. van de - \ 2010
De Fruitteelt 100 (2010)13. - ISSN 0016-2302 - p. 8 - 9.
appels - peren - fruitbewaarplaatsen - oogstvoorspelling - kwaliteitscontroles - rassen (planten) - opslagloodsen - kwaliteit - monitoring - apples - pears - fruit stores - yield forecasting - quality controls - varieties - stores - quality
Van de hoofdrassen worden de kwaliteitsverwachtingen en de te adviseren bewaarcondities voor het komende seizoen belicht. Daarnaast wordt teruggeblikt op het afgelopen bewaarseizoen 2009-2010, dat in kwalitatief opzicht tot op heden een succesvol verloop heeft.
Research activities in regional crop modelling and yield forecasting
Wit, A.J.W. de; Diepen, C.A. van; Boogaard, H.L. - \ 2009
Agro Informatica 22 (2009)2. - ISSN 0925-4455 - p. 25 - 27.
gewasopbrengst - oogstvoorspelling - weer - remote sensing - monitoring - modellen - neerslag - gewasgroeimodellen - gewasmonitoring - crop yield - yield forecasting - weather - remote sensing - monitoring - models - precipitation - crop growth models - crop monitoring
CGMS is being applied successfully within the MARS Crop Yield Forecasting System for qualitative monitoring of the growing season and for making quantitative crop yield forecasts. Nevertheless, there are large uncertainties related to applying crop growth models over large areas.
IT architecture of the MARS crop yield forecasting system
Lokers, R.M. ; Kraalingen, D.W.G. van; Boogaard, H.L. - \ 2009
Agro Informatica 22 (2009)2. - ISSN 0925-4455 - p. 21 - 23.
gewasopbrengst - oogstvoorspelling - weer - remote sensing - monitoring - gewasmonitoring - gewasgroeimodellen - crop yield - yield forecasting - weather - remote sensing - monitoring - crop monitoring - crop growth models
The Crop Growth Monitoring System (CGMS) provides operational services and analysis tools to the Joint Research Centre of the European Commission (JRC) in the area of crop monitoring and crop yield forecast, as part the MARS Crop Yield Forecasting System.
The CMGS statistical tool for yield forecasting
Hoek, S.B. ; Goedhart, P.W. ; Akkermans, L.M.W. - \ 2009
Agro Informatica 22 (2009)2. - ISSN 0925-4455 - p. 19 - 20.
gewasopbrengst - oogstvoorspelling - remote sensing - modellen - statistische analyse - gewasmonitoring - biostatistiek - crop yield - yield forecasting - remote sensing - models - statistical analysis - crop monitoring - biostatistics
Official EU forecasts for crop yields are calculated several times per year by crop analysts of the Joint Research Centre (JRC). In general the forecasts for a certain region are based on statistical models which describe historical yields in terms of a time trend combined with a relationship with CGMS indicator data. Since 1994 the so-called CGMS statistical module has been in use at JRC - to facilitate crop yield forecasting at national and sub-national level. Now, an improved version has been developed, for use by the crop analysts of JRC. The tool was developed by two subdivisions of the Wageningen UR: Alterra-CGI and Biometris. Aim was to enable the analysts to construct more elaborate models (regression and scenario models) than they could construct with the previous module and to make it easier to include more indicators, i.e. weather indicator and remote sensing data.
Crop models: main developments, their use in CGMS and integrated modeling
Wolf, J. ; Ittersum, M.K. van - \ 2009
Agro Informatica 22 (2009)2. - ISSN 0925-4455 - p. 15 - 18.
gewassen - groeimodellen - oogstvoorspelling - gewasopbrengst - monitoring - wiskundige modellen - gewasgroeimodellen - crops - growth models - yield forecasting - crop yield - monitoring - mathematical models - crop growth models
Het artikel beschrijft de voornaamste ontwikkelingen in gewasgroeimodellen (WOFOST), hun gebruik in CGMS en geïntegreerde modellering
History of CGMS in the MARS project
Diepen, C.A. van; Boogaard, H.L. - \ 2009
Agro Informatica 22 (2009)2. - ISSN 0925-4455 - p. 11 - 14.
gewassen - oogstvoorspelling - weer - monitoring - remote sensing - onderzoeksprojecten - geschiedenis - gewasgroeimodellen - gewasmonitoring - crops - yield forecasting - weather - monitoring - remote sensing - research projects - history - crop growth models - crop monitoring
The MARS project (Monitoring Agriculture with Remote Sensing) was initiated by the European Commission (EC) in 1988 as a research programme in which three Directorates were involved: DG-Agriculture, Eurostat and the Joint Research Centre (JRC)
Overview CGMS and related tools
Boogaard, H.L. ; Wijngaart, R. van der; Diepen, C.A. van - \ 2009
Agro Informatica 22 (2009)2. - ISSN 0925-4455 - p. 8 - 10.
gewassen - oogstvoorspelling - weer - gewasopbrengst - monitoring - gewasgroeimodellen - crops - yield forecasting - weather - crop yield - monitoring - crop growth models
The main purpose of Crop Growth Monitoring System CGMS is to estimate the influence of weather conditions on crop growth and yield on regional scale (provinces, countries, continents). Therefore, CGMS combines aspects of both weather data processing and collection as well as modelling crop growth and development.
|Aandachtspunten voor bewaarseizoen 2008/'09
Schaik, A.C.R. van; Geijn, F.G. van de - \ 2008
De Fruitteelt 98 (2008)35. - ISSN 0016-2302 - p. 10 - 11.
appels - peren - rassen (planten) - fruitbewaarplaatsen - oogstvoorspelling - kwaliteit - koudeopslag - richtlijnen (guidelines) - apples - pears - varieties - fruit stores - yield forecasting - quality - cold storage - guidelines
De hardfruitoogst van 2008 staat voor de deur. In dit artikel worden van de hoofdrassen de kwaliteitsverwachtingen en de te adviseren bewaarcondities voor het komende seizoen belicht. Daarnaast wordt teruggeblikt op het afgelopen bewaarseizoen
|Bij Conference moet vooral de einddatum niet overschreden worden
Schaik, A.C.R. van - \ 2007
De Fruitteelt 97 (2007)33/34. - ISSN 0016-2302 - p. 8 - 9.
fruitteelt - fruitgewassen - peren - houdbaarheid (kwaliteit) - koudeopslag - oogstvoorspelling - plukken (picking) - oogsten - fruit growing - fruit crops - pears - keeping quality - cold storage - yield forecasting - picking - harvesting
De oogst van Conference is in 2007 vroeger dan in 2006, zeker een dag of tien. Gezien de diverse kwaliteitsproblemen die in 2006 speelden, is het goed om een aantal zaken rondom het pluktijdstip nader toe te lichten. Het afgelopen bewaarseizoen kende diverse kwaliteitsproblemen bij de peren, zoals zachte vruchten (floepers), buikziek, hol en bruin, slappe nekken en een verkort uitstalleven. Deze problemen zijn deels toe te schrijven aan het bijzondere groeiseizoen
Regional crop yield forecasting using probalistic crop growth modelling and remote sensing data assimilation
Wit, A.J.W. de - \ 2007
Wageningen University. Promotor(en): Michael Schaepman, co-promotor(en): Paul Torfs; Sytze de Bruin. - [S.l.] : S.n. - ISBN 9789085047094 - 154
gewasopbrengst - oogstvoorspelling - remote sensing - gewassen - groei - weer - modellen - gewasgroeimodellen - crop yield - yield forecasting - remote sensing - crops - growth - weather - models - crop growth models
Een belangrijk onderdeel van het MARS oogstvoorspellingssysteem is het zogenaamde CGMS (crop growth monitoring system). CGMS gebruikt een gewasgroeimodel om het effect van bodem, weer en teeltmaatregelen op de groei van het gewas te bepalen. Hiervoor worden relevante gegevens verzameld over Europa. Op basis van deze gegevens simuleert het model WOFOST de gewasgroei. In dit proefschrift wordt op praktische en theoretische gronden beargumenteerd dat de onzekerheid in het weer de bepalende factor is voor onzekerheid in de WOFOST simulaties. Dit komt omdat de weersgegevens afkomstig zijn van een beperkt aantal weerstations over Europa en met deze reeks is het niet mogelijk om de daadwerkelijke ruimtelijke en temporele variabiliteit in het weer te beschrijven
Oogstvoorspeller paprika: Ontwikkeling van een model en internetapplicatie voor teeltregistratie en aanvoervoorspelling bij paprika
Buwalda, F. - \ 2004
Naaldwijk : Praktijkonderzoek Plant & Omgeving, Sector Glastuinbouw (Rapporten PPO ) - 62
capsicum annuum - gewasopbrengst - oogstvoorspelling - nederland - paprika - capsicum annuum - crop yield - yield forecasting - netherlands - sweet peppers
|Ontwikkeling van niet-destructieve methoden ten behoeve van de oogstvoorspelling en teeltbegeleiding in de fabrieksaardappelteelt : derde evaluatie van SPAD- en Cropscan-metingen in de proefvelden KB9036 en KP9060, 2000
Velvis, H. ; Haren, R.F.J. van; Begeman, J.R. - \ 2002
Wageningen : Plant Research International (Nota / Plant Research International 147) - 20
aardappelen - methodologie - niet-destructief testen - oogstvoorspelling - teelt - proefvelden - fabrieksaardappelen - potatoes - methodology - nondestructive testing - yield forecasting - cultivation - experimental plots - starch potatoes
Regional analysis of maize-based land use systems for early warning applications
Rugege, D. - \ 2002
Wageningen University. Promotor(en): J. Bouma; A.K. Skidmore; P.M. Driessen. - S.l. : S.n. - ISBN 9789058085849 - 121
landgebruik - landevaluatie - regionale planning - regionale verkenningen - gewasproductie - gewasopbrengst - remote sensing - oogstvoorspelling - simulatiemodellen - groeianalyse - groeimodellen - verliezen - voedselproductie - maïs - zimbabwe - land use - land evaluation - regional planning - regional surveys - crop production - crop yield - remote sensing - yield forecasting - simulation models - growth analysis - growth models - losses - food production - maize - zimbabwe
Conventional analytical crop growth models cannot handle actual Land Use Systems because of massive data needs, algorithm complexity and prohibitive error propagation. It is possible however to describe rigidly simplified 'Production Situations' representing Land Use Systems with annual row crops and minimal environmental constraints. The simplest Production Situation imaginable is a Land Use System in which all constraints that can be eliminated by a farmer are indeed (assumed to be) eliminated. Crop growth and yield are then entirely conditioned by crop physiology and weather conditions, notably by the temperature and radiation during the crop cycle. The calculated production level is not the actual production but the production potential.
In many countries, water availability to the crop is the main constraint to crop production. The biophysical production potential model has therefore been extended with a water budget routine that matches actual water use with the crop's requirement in order to calculate the "water-limited production potential". In this configuration, crop physiology, temperature, radiation and water availability condition the calculated level of crop (potential) production. This thesis discusses the use of satellite-derived rainfall data for regional analysis of water-limited yield potentials.
Monitoring and crop yield forecasting for early warning applications require insight in farmers' reality. Often, a score of environmental and socio-economic constraints reduce on-farm production to a level that lags far behind the theoretical production potential. This thesis explores farmers' insights, in an attempt to identify the causes and structure of the "yield gap" between potential (reference) production levels and production levels realized on-farm.
So far, actual production could only be established through field measurements. This thesis presents a methodology for estimating regional levels of actual crop production. The difference between remotely sensed canopy temperature and ambient temperature is used to estimate the degree of stomata closure of the crop. Introducing this Remote Sensing based degree of stomata closure in calculations of assimilatory activity permits to calculate the actual rate of crop growth over regions.
Repeated measurements during the crop cycle allow monitoring of the sufficiency of actual management practices. Introducing estimated or forecast weather data in crop growth calculations for the remainder of the crop cycle permits to make repeated estimates of anticipated crop production and to timely signal a need for remedial action.
|Application of NOAA-AVHRR satellite images for the measurement of surface temperatures in relation to crop growth modelling
Meijerink, B.H.A. - \ 2002
Wageningen : Alterra - 75 p.
remote sensing - aardoppervlak - droogteschade - geo-informatie - gewasgroei - gewasproductie - landbouw - meteorologie - oogstvoorspelling - satellietbeelden - simulatiemodel - verdamping - Europa
|Ontwikkeling van niet-destructieve methoden ten behoeve van de oogstvoorspelling en teeltbegeleiding in de fabrieksaardappelteelt : tweede evaluatie van SPAD- en Cropscan-metingen in de proefvelden KB9020 en KP9039, 1999
Velvis, H. ; Haren, R.F.J. van; Begeman, J.R. - \ 2000
Wageningen : Plant Research International (Nota / Plant Research International 41) - 26
aardappelen - methodologie - niet-destructief testen - oogstvoorspelling - teelt - proefvelden - fabrieksaardappelen - potatoes - methodology - nondestructive testing - yield forecasting - cultivation - experimental plots - starch potatoes