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Book ; Online: Global maps of agricultural expansion potential at a 300 m resolution

Čengić, M. / Steinmann, Zoran / Defourny, P. / Doelman, Jonathan / Lamarche, Céline / Stehfest, Elke / Schipper, Aafke M. / Huijbregts, Mark A.J.

2023

Abstract: Global maps of agricultural expansion potential at a 300 m resolution This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study. Abstract: The global expansion of agricultural land is a leading ... ...

Abstract	Global maps of agricultural expansion potential at a 300 m resolution This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study. Abstract: The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with 50% crops, and (iii) mosaics with 50% crops, and mosaics with 50% crops, and 40 - mosaics with 50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with 50% crops, and category 40 is mosaics with
Keywords	GLOBIO ; Land cover change ; agriculture ; biodiversity ; cropland ; deforestation ; integrated assessment models ; sustainability
Subject code	333
Publisher	Radboud Universiteit
Publishing country	nl
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: In-Season Potato Crop Nitrogen Status Assessment from Satellite and Meteorological Data

Goffart, D. / Abdallah, F. Ben / Curnel, Y. / Planchon, V. / Defourny, P. / Goffart, J.-P.

Potato research. 2022 Sept., v. 65, no. 3

2022

Abstract: For a conventional potato crop, splitting nitrogen (N) application is recognised as an efficient strategy to improve tuber yield and quality and to mitigate N losses to the environment. This approach requires the assessment of in-season crop N status for ...

Abstract	For a conventional potato crop, splitting nitrogen (N) application is recognised as an efficient strategy to improve tuber yield and quality and to mitigate N losses to the environment. This approach requires the assessment of in-season crop N status for decisions on supplemental mineral N fertiliser application. This study focuses on the assessment of potato crop biophysical variables useful to establish crop N status. Field, satellite and meteorological data were collected in farmer’s fields during 3 years (2017–2019) with contrasted meteorological conditions. Degree days (DD) and water balance from planting date were computed from meteo data, and a selection of relevant vegetation indices (VIs) was derived from Sentinel-2 reflectance. Multiple linear regression (MLR) and random forest regression (RFR) models predicting shoots biomass, shoots N content and shoots N uptake from a combination of meteo and/or satellite-based variables were defined and evaluated. The best combinations integrate DD and two to four VIs and perform with cross-validation RMSE of about 0.38 DM t ha⁻¹, 0.41%, 21 kg ha⁻¹ for MLR and 0.32 DM t ha⁻¹, 0.31%, 19 kg ha⁻¹ for RFR. Despite these performances, MLR was shown to be more robust. From these estimated variables, two methods are proposed to derive total N uptake and nitrogen nutrition index. The most relevant method uses shoots N uptake and biomass. It allows future estimation of in-season supplemental N fertiliser to be applied to reach a targeted tuber yield.
Keywords	algorithms ; biomass ; farmers ; fertilizer application ; meteorological data ; nitrogen ; nitrogen fertilizers ; nutrition ; potatoes ; reflectance ; regression analysis ; research ; satellites ; total nitrogen ; vegetation
Language	English
Dates of publication	2022-09
Size	p. 729-755.
Publishing place	Springer Netherlands
Document type	Article
ZDB-ID	407680-1
ISSN	1871-4528 ; 0014-3065
ISSN (online)	1871-4528
ISSN	0014-3065
DOI	10.1007/s11540-022-09545-0
Database	NAL-Catalogue (AGRICOLA)

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Article: Field-scale assessment of Belgian winter cover crops biomass based on Sentinel-2 data

Goffart, D. / Curnel, Y. / Planchon, V. / Goffart, J.-P. / Defourny, P.

European journal of agronomy

2021 Volume 126, Issue -, Page(s) 126278

Language	English
Document type	Article
ZDB-ID	1157136-6
ISSN	1161-0301
Database	Current Contents Nutrition, Environment, Agriculture

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Article ; Online: Ethiopian Crop Type 2020 (EthCT2020)

Blasch, Gerald / Alemayehu, Yoseph / Lesne, Louise / Wolter, Jolan / Taymans, Matthieu / Tesfaye, Tsegaab / Negash, Tamirat / Andulalem, Mequanint / Gutu, Kitessa / Debela, Megersa / Eshetu, Zerihun / Tesfaye, Kindie / Mottaleb, Khondoker / Defourny, Pierre / Hodson, David P

Data in brief

2024 Volume 54, Page(s) 110427

Abstract: Crop type observation is crucial for various environmental and agricultural remote sensing applications including land use and land cover mapping, crop growth monitoring, crop modelling, yield forecasting, disease surveillance, and climate modelling. ... ...

Abstract	Crop type observation is crucial for various environmental and agricultural remote sensing applications including land use and land cover mapping, crop growth monitoring, crop modelling, yield forecasting, disease surveillance, and climate modelling. Quality-controlled georeferenced crop type information is essential for calibrating and validating machine learning algorithms. However, publicly available field data is scarce, particularly in the highly dynamic smallholder farming systems of sub-Saharan Africa. For the 2020/21 main cropping season (
Language	English
Publishing date	2024-04-14
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2786545-9
ISSN	2352-3409 ; 2352-3409
ISSN (online)	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2024.110427
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Accuracy Requirements for Early Estimation of Crop Production in Senegal

Jacques, Damien Christophe / Defourny, Pierre

2019

Abstract: Early warning systems for food security rely on timely and accurate estimations of crop production. Several approaches have been developed to get early estimations of area and yield, the two components of crop production. The most common methods, based ... ...

Abstract	Early warning systems for food security rely on timely and accurate estimations of crop production. Several approaches have been developed to get early estimations of area and yield, the two components of crop production. The most common methods, based on Earth observation data, are image classification for crop area and correlation with vegetation index for crop yield. Regardless of the approach used, early estimators of cropland area, crop area or crop yield should have an accuracy providing lower production error than existing historical crop statistics. The objective of this study is to develop a methodological framework to define the accuracy requirements for early estimators of cropland area, crop area and crop yield in Senegal. These requirements are made according to (i) the inter-annual variability and the trend of historical data, (ii) the calendar of official statistics data collection, and (iii) the time at which early estimations of cropland area, crop area and crop yield can theoretically be available. This framework is applied to the seven main crops in Senegal using 20 years of crop production data. Results show that the inter-annual variability of crop yield is the main factor limiting the accuracy of pre-harvest production forecast. Estimators of cropland area can be used to improve production prediction of groundnuts, millet and rice, the three main crops in Senegal stressing the value of cropland mapping for food security. While applied to Senegal, this study could easily be reproduced in any country where reliable agricultural statistics are available.
Keywords	Computer Science - Computers and Society
Subject code	333
Publishing date	2019-06-09
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Constraining water limitation of photosynthesis in a crop growth model with sun-induced chlorophyll fluorescence

De Cannière, S. / Herbst, M. / Vereecken, H. / Defourny, P. / Jonard, F.

Remote sensing of environment. 2021 Dec. 15, v. 267

2021

Abstract: Water fulfils key roles in maintaining a plant's biological activity. Water shortage induces stomatal closure, causing a reduction in photosynthesis and transpiration rates. Sun-induced chlorophyll fluorescence (SIF) emission is sensitive to subtle, ... ...

Abstract	Water fulfils key roles in maintaining a plant's biological activity. Water shortage induces stomatal closure, causing a reduction in photosynthesis and transpiration rates. Sun-induced chlorophyll fluorescence (SIF) emission is sensitive to subtle, stress-induced variations in non-photochemical quenching and in photosynthetic electron transport, caused by e.g., a fluctuation in the water availability. Based on this sensitivity, a framework for calibrating a water stress function in a crop growth model using ground-based SIF observations is proposed. SIF time series are simulated by coupling the AgroC crop growth model to the Soil Canopy Observations Photosynthesis Energy (SCOPE) model. This allowed parametrizing the water stress function in the AgroC crop growth model, resulting in improved estimates of actual evapotranspiration and net ecosystem exchange over a sugar beet stand during stressed periods. The improvement in the estimation of the water and carbon fluxes by AgroC during the summer months highlights the ability of canopyscale SIF observations to serve as a remote sensing metric to indicate the intensity of a stress condition. We argue that our framework, linking SIF emission to stress functions, can be used to extract information concerning drought stress from the Fluorescence Explorer (FLEX) satellite, scheduled for launch in 2024.
Keywords	bioactive properties ; canopy ; carbon ; chlorophyll ; crop models ; energy ; environment ; evapotranspiration ; fluorescence ; net ecosystem exchange ; photosynthetic electron transport ; satellites ; soil ; stomatal movement ; sugar beet ; summer ; time series analysis ; transpiration ; water shortages ; water stress
Language	English
Dates of publication	2021-1215
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	431483-9
ISSN	0034-4257
ISSN	0034-4257
DOI	10.1016/j.rse.2021.112722
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: The Forests of the Congo Basin

Eba’a Atyi, R. / Hiol Hiol, F. / Lescuyer, G. / Mayaux, P. / Defourny, P. / Bayol, N. / Saracco, F. / Pokem, D. / Sufo Kankeu, R. / Nasi, R.

State of the Forests 2021

2023

Keywords	forest management ; climate change ; conservation ; ecosystem services
Language	English
Publishing date	2023-01-24T07:05:29Z
Publisher	CIFOR
Publishing country	fr
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Validating a continental-scale groundwater diffuse pollution model using regional datasets.

Ouedraogo, Issoufou / Defourny, Pierre / Vanclooster, Marnik

Environmental science and pollution research international

2017 Volume 26, Issue 3, Page(s) 2105–2119

Abstract: In this study, we assess the validity of an African-scale groundwater pollution model for nitrates. In a previous study, we identified a statistical continental-scale groundwater pollution model for nitrate. The model was identified using a pan-African ... ...

Abstract	In this study, we assess the validity of an African-scale groundwater pollution model for nitrates. In a previous study, we identified a statistical continental-scale groundwater pollution model for nitrate. The model was identified using a pan-African meta-analysis of available nitrate groundwater pollution studies. The model was implemented in both Random Forest (RF) and multiple regression formats. For both approaches, we collected as predictors a comprehensive GIS database of 13 spatial attributes, related to land use, soil type, hydrogeology, topography, climatology, region typology, nitrogen fertiliser application rate, and population density. In this paper, we validate the continental-scale model of groundwater contamination by using a nitrate measurement dataset from three African countries. We discuss the issue of data availability, and quality and scale issues, as challenges in validation. Notwithstanding that the modelling procedure exhibited very good success using a continental-scale dataset (e.g. R
MeSH term(s)	Africa ; Environmental Monitoring/methods ; Fertilizers/analysis ; Groundwater/chemistry ; Humans ; Models, Statistical ; Multivariate Analysis ; Nitrates/analysis ; Nitrogen ; Reproducibility of Results ; Water Pollutants, Chemical/analysis ; Water Pollution/analysis
Chemical Substances	Fertilizers ; Nitrates ; Water Pollutants, Chemical ; Nitrogen (N762921K75)
Language	English
Publishing date	2017-12-11
Publishing country	Germany
Document type	Journal Article ; Meta-Analysis ; Validation Studies
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-017-0899-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Optimal sample size and composition for crop classification with Sen2-Agri’s random forest classifier

Schulthess, Urs / Rodrigues, Francelino / Taymans, Matthieu / Bellemans, Nicolas / Bontemps, Sophie / Ortiz Monasterio, Jose Iván / Gerard, Bruno G. / Defourny, Pierre

Remote Sensing

2023

Abstract: Sen2-Agri is a software system that was developed to facilitate the use of multi-temporal satellite data for crop classification with a random forest (RF) classifier in an operational setting. It automatically ingests and processes Sentinel-2 and LandSat ...

Abstract	Sen2-Agri is a software system that was developed to facilitate the use of multi-temporal satellite data for crop classification with a random forest (RF) classifier in an operational setting. It automatically ingests and processes Sentinel-2 and LandSat 8 images. Our goal was to provide practitioners with recommendations for the best sample size and composition. The study area was located in the Yaqui Valley in Mexico. Using polygons of more than 6000 labeled crop fields, we prepared data sets for training, in which the nine crops had an equal or proportional representation, called Equal or Ratio, respectively. Increasing the size of the training set improved the overall accuracy (OA). Gains became marginal once the total number of fields approximated 500 or 40 to 45 fields per crop type. Equal achieved slightly higher OAs than Ratio for a given number of fields. However, recall and F-scores of the individual crops tended to be higher for Ratio than for Equal. The high number of wheat fields in the Ratio scenarios, ranging from 275 to 2128, produced a more accurate classification of wheat than the maximal 80 fields of Equal. This resulted in a higher recall for wheat in the Ratio than in the Equal scenarios, which in turn limited the errors of commission of the non-wheat crops. Thus, a proportional representation of the crops in the training data is preferable and yields better accuracies, even for the minority crops.
Keywords	crops ; forests ; machine learning ; agriculture ; remote sensing
Language	English
Publishing date	2023-02-03T08:30:07Z
Publisher	MDPI
Publishing country	fr
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Global Maps of Agricultural Expansion Potential at a 300 m Resolution

Čengić, Mirza / Steinmann, Zoran J.N. / Defourny, Pierre / Doelman, Jonathan C. / Lamarche, Céline / Stehfest, Elke / Schipper, Aafke M. / Huijbregts, Mark A.J.

Land

2023 Volume 12, Issue 3

Abstract: The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address ... ...

Abstract	The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using artificial neural network (ANN) models relating locations of recent past conversions (2007–2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil, and accessibility. Cross-validation of the models indicated good performance with area under the curve (AUC) values of 0.88–0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83–0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.
Keywords	GLOBIO ; agriculture ; biodiversity ; cropland ; deforestation ; integrated assessment models ; land-cover change ; sustainability
Language	English
Publishing country	nl
Document type	Article ; Online
ZDB-ID	2682955-1
ISSN	2073-445X
ISSN	2073-445X
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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