Context Assessment for Agroecology Transformation in the Tunisian Living Landscape

The purpose of this Context Assessment is threefold: first, to characterize the environmental, social and economic and political contexts of the Tunisian ALL; second, to understand the data and information currently available in sub-region of the ALL, and third to characterize the extent to which agroecological principles are already being employed locally at the ALL levels. This report constitutes a basis of information and discussion to conduct the impact assessment. It is also valuable to all WPs in the Initiative as it provides critical quantitative or qualitative data and information regarding capacities assessment, policy influence, and other environmental attributes which can guide the initiative implementation and impact in 2023/2024. The present Context Assessment in Tunisia has been elaborated from primary and secondary sources of data. The primary sources of data are issued from focus groups and formal and informal interviews conducted in the targeted area between June and December 2022, as part of WP1 and WP4 activities. The secondary sources of data came from previous research and development projects, in addition to formal and grey literature or technical reports and policy documents. This report will be enriched with a household survey planned during the first quarter of 2023. This report contributes to Output 2.1. Baseline – current conditions of agricultural systems of small holder farmers in each ALL, Output 1.1 on establishment of the ALL, Output 4.1 on the identification of policies and local institutions and their role in the AE pathways

Sentinel 1 response to cereal leaf area index (lai): study case for central tunisia

Leaf area index (LAI) is very used to reveal the vegetation situation. To estimate the LAI for cereal, both direct and indirect methods have been used. In particular, remote sensing is a fast and reliable technique to develop the LAI estimation models. In this work, we present the potential of Sentinel 1 images for cereal estimation LAI in the center of Tunisia under semi arid climate. We established a statistical relationship between field LAI measurement for irrigated and rainfed wheat and backscatter coefficient based on an empirical analysis. This will be very useful in order to predict the water stress in a subsequent step. For experimental validation, the LAI of wheat crops were determined through the crop growth stages using eight Sentinel-1 images. The results showed a significant correlations, for the irrigated wheat, between LAI and backscatter coefficient for VV polarization (r values of -0.7) and for HV (r value of -0.5). For the rainfed wheat only the VV polarization showed a significant correlation (r= 0.6)

Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas

Mapping and monitoring land use (LU) changes is one of the most effective ways to understand and manage land transformation. The main objectives of this study were to classify LU using supervised classification methods and to assess the effectiveness of various machine learning methods. The current investigation was conducted in the Nord-Est area of Tunisia, and an optical satellite image covering the study area was acquired from Sentinel-2. For LU mapping, we tested three machine learning models algorithms: Random Forest (RF), K-Dimensional Trees K-Nearest Neighbors (KDTree-KNN) and Minimum Distance Classification (MDC). According to our research, the RF classification provided a better result than other classification models. RF classification exhibited the best values of overall accuracy, kappa, recall, precision and RMSE, with 99.54%, 0.98%, 0.98%, 0.98% and 0.23%, respectively. However, low precision was observed for the MDC method (RMSE = 1.15). The results were more intriguing since they highlighted the value of the bare soil index as a covariate for LU mapping. Our results suggest that Sentinel-2 combined with RF classification is efficient for creating a LU map

Registration of ‘Krib’ new lentil variety in Tunisia

In Tunisia, lentil (Lens culinaris subsp. culinaris Medik.) is cultivated in arid and semi-arid areas. Terminal drought and heat stresses during the end of cycle causes signifcant yield losses. Selec tion of short cycle varieties could be the best option to escape the efect of pre cited abiotic factors. ‘krib’ was developed using both modifed pedigree and bulk methods by INRAT in collaboration with ICARDA. It’s a selection from an advanced F9 line, FLIP2012 196L, which was derived from a cross of ILL590 (early-maturing genotype) with ILL8113 (drought tolerant). During the three consecutive cropping seasons (2014–2017), the increase in yield of ‘krib’ above that of the local checks was about 13 and 15% for ‘kef’ and ‘Boulifa’, respectively. ‘Krib’ is an early fowering (on average~93 d) and early-maturing (on average~138 d) variety. It’s therefore, the earliest variety among the previously released varieties. Its dehulled seed has a protein content of 26.8% which was above that of ‘Kef’ (23.9%). ‘Krib’ showed good agronomic performance under drought conditions and large adaptation for the Tunisian environments. Tested as ILL11171, ‘Krib’ was approved for release and registered in the Tunisian Ofcial Catalogue of Plant Varieties in 2019, based on the superior perfor mance, by the DG/PCQPA, Tunisia. The availability of ‘Krib’ variety to farmers could increase lentil pro duction and ofers the producer aproftable beneft in their cropping systems

The Potential of Using Radarsat-2 Satellite Image for Modeling and Mapping Wheat Yield in a Semiarid Environment

The monitoring of cereal productions, mainly through yield estimations, has played an important role in providing reliable information to decision makers in order to ensure the proper management of agricultural markets. In this context, remote sensing, which allows the coverage of large areas, is an important source of information that complements those obtained by other methods. In this study, we aim to estimate the wheat yield at an early growth stage (spring season) using only one Radarsat-2 (RS-2) polarimetric image. We propose an empirical statistical relationship between the yield measured in situ and polarimetric parameters extracted from the RS-2 image. The RS-2 image was acquired at the flowering stage as it is proved to be the most appropriate moment for yield prediction. We selected the region of Boussalem in the northwest of Tunisia as the study area. For experimental validation, the yield was determined in situ at the end of the wheat season. Results showed that the polarization ratios are more correlated than the polarimetric parameters with the grain yield with a significant correlation of the HH/VV ratio (r = 0.76) and the HV/VV ratio (r = −0.75), while the most correlated polarimetric parameter was Alpha (r = −0.51). Finally, the multiple regression has led to the development of a three-variable model (HH/VV, HV/HH, and alpha) as the best predictor of the wheat grain yields. Validation results revealed a great potential with a determination coefficient (R2) of 0.58 and root mean squared error (RMSE) of 0.89 t/ha

The Potential of Using Radarsat-2 Satellite Image for Modeling and Mapping Wheat Yield in a Semiarid Environment

The monitoring of cereal productions, mainly through yield estimations, has played an important role in providing reliable information to decision makers in order to ensure the proper management of agricultural markets. In this context, remote sensing, which allows the coverage of large areas, is an important source of information that complements those obtained by other methods. In this study, we aim to estimate the wheat yield at an early growth stage (spring season) using only one Radarsat-2 (RS-2) polarimetric image. We propose an empirical statistical relationship between the yield measured in situ and polarimetric parameters extracted from the RS-2 image. The RS-2 image was acquired at the flowering stage as it is proved to be the most appropriate moment for yield prediction. We selected the region of Boussalem in the northwest of Tunisia as the study area. For experimental validation, the yield was determined in situ at the end of the wheat season. Results showed that the polarization ratios are more correlated than the polarimetric parameters with the grain yield with a significant correlation of the HH/VV ratio (r = 0.76) and the HV/VV ratio (r = −0.75), while the most correlated polarimetric parameter was Alpha (r = −0.51). Finally, the multiple regression has led to the development of a three-variable model (HH/VV, HV/HH, and alpha) as the best predictor of the wheat grain yields. Validation results revealed a great potential with a determination coefficient (R2) of 0.58 and root mean squared error (RMSE) of 0.89 t/ha

Wheat Water Deficit Monitoring Using Synthetic Aperture Radar Backscattering Coefficient and Interferometric Coherence

Due to the climate change situation, water deficit stress is becoming one of the main factors that threatens the agricultural sector in semi-arid zones. Thus, it is extremely important to provide efficient tools of water deficit monitoring and early detection. To do so, a set of Synthetic Aperture Radar (SAR) backscattering and interferometric SAR (InSAR) Sentinel-1 data, covering the period from January to June 2016, are considered over a durum wheat field in Tunisia. We first studied the temporal variation of the InSAR coherence data and the SAR backscattering coefficient as a function of the phenological stage of the wheat. Subsequently, the parameters of the SAR and InSAR coherence images were analyzed with regard to the water stress coefficient and the wheat height variations. The main findings of this study highlight the high correlation (r = 0.88) that exists between the InSAR coherence and the water stress coefficient, on the one hand, and between the backscattering coefficient, the interferometric coherence, and the water deficit coefficient (R2 = 0.95 and RMSE = 14%), on the other hand. When a water deficit occurs, the water stress coefficient increases, the crop growth decreases, and the height variation becomes low, and this leads to the increase of the InSAR coherence value. In summary, the reliability of Sentinel-1 SAR and InSAR coherence data to monitor the biophysical parameters of the durum wheat was validated in the context of water deficits in semi-arid regions