OBSERVATIONS, PERCEPTIONS ET ADAPTATIONS AU CHANGEMENT CLIMATIQUE DANS LA SOUS-PRÉFECTURE DE SÉGUÉLA (CÔTE D’IVOIRE)

In a context of sustainable development and food security, the fight against climate change has become a priority. In tropical rural areas, its effects are beginning to appear by affecting agricultural production, which is a factor that aggravates socio-economic impacts. Indeed, understanding the determinisms of climate change as well as their perception and the adaptation strategies developed by rural populations is an issue of food security and socio-economic development. In the sub-prefecture of Séguéla, the perceptions and adaptations to climate change of populations living from rain-fed agriculture are acute. This article analyzes on the one hand the climatic variability of this sub-prefecture based on scientific observations of the annual accumulations of precipitation from 1986 to 2015. And on the other hand, following a questionnaire survey, the perception and representation of this climatic variability as well as the strategies developed in response by farmers were noted. The main results indicate a decrease in rainfall in general modifying the crop calendar of farmers. In addition, the study attests that peasant perceptions and representations are linked to their local beliefs. This then results in dynamics and modalities of adaptation through the introduction of new cultures / associations of cultures, the development of lowlands reflecting the interactions of the peasant population with its environment.

AREA ESTIMATION OF COTTON AND MAIZE CROPS IN PERAMBALUR DISTRICT OF TAMIL NADU USING MULTI DATE SENTINEL-1A SAR DATA & OPTICAL DATA

<p><strong>Abstract.</strong> This study was carried out to estimate the area of cotton and maize crops in Permabalur district of Tamil Nadu using microwave and optical data. Permabalur was selected as the study area, as it is the largest cotton and maize producing district in Tamil Nadu. The multi-temporal Sentinel-1A SAR data was acquired from 09th July, 2016 to 17th January, 2017 as it coincides with the crop calendar of these crops. Both the Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarized data were compared. The cloud free Landsat 8 data acquired on 7th October 2016 was fused with the Vertical–Vertical (VV) and Vertical-Horizontal (VH) polarized data of 13th October and classified. Unsupervised classification approach was adopted to classify the cotton and maize pixels. The highest accuracy of 72.73% and 76.24% were achieved in VV polarization + Landsat 8 data and VH polarization + Landsat 8 data respectively. The cotton and maize areas were estimated to be 20,218&amp;thinsp;ha and 28,032&amp;thinsp;ha respectively. It is also evident that VH polarization fused with optical data is better in discriminating the cotton and maize crop than VV polarization fused with optical data.</p>

Mapping Crop Calendar Events and Phenology-Related Metrics at the Parcel Level by Object-Based Image Analysis (OBIA) of MODIS-NDVI Time-Series: A Case Study in Central California

Remote sensing technology allows monitoring the progress of vegetation and crop phenology in large regions. Seasonal vegetation trends are commonly estimated from high temporal resolution but coarse spatial resolution satellite imagery, e.g., from MODIS-NDVI (Moderate Resolution Imaging Spectroradiometer—Normalized Difference Vegetation Index) time-series, which has usually limited their application to scenarios with few land uses or crops covering areas larger than actual parcel sizes. As an alternative, this paper proposes a general and robust procedure to map crop phenology at the level of individual crop parcels, and validates its feasibility in a complex and diverse cropland area located in central California. A first calibration phase consisted of evaluating the three curve-fitting models implemented in the TIMESAT software (i.e., asymmetric Gaussian (AG), double logistic (DL), and adaptive Savitzky–Golay (SG) filtering) and reporting the model and its settings that best adjusted to the MODIS-NDVI profile of each crop studied. Next, based on the selected crop-specific models and with a crop map previously obtained from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-temporal images, the procedure mapped four crop calendar events (i.e., start, end, middle, and length of the season) and five phenology-related metrics (i.e., base, maximum, amplitude, derivatives, and integrals of the NDVI values) of the study region by object-based image analysis (OBIA) of the MODIS-NDVI time-series. To mitigate the impact of mixed pixels, the OBIA procedure was designed to automatically apply a restrictive criterion based on the coverage of MODIS-NDVI pixels in each crop parcel: (1) using only the MODIS-NDVI pixels that were placed 100% within each crop parcel (i.e., “pure” pixels); or (2) if no “pure” pixels exist in any crop parcel, using only pixels with coverage percentages greater than 50%, and in such cases, reporting the mixing percentage in the output file. The calibration phase showed that the performance of the SG filtering was superior in most crops, with the exception of rice, while the AG model was intermediate in all of the cases. Differences between the dates of the start and end of the season that were observed in 120 ground-truth fields and the ones estimated by the crop-specific models were in a range of 11 days (for the corn fields) and 22 days (for the vineyard fields) on average. The OBIA procedure was also validated in 240 independent parcels with “pure” MODIS-NDVI pixels, reporting 89% and 82% of accuracy when mapping the start and end of the season, respectively. Our results revealed different growth patterns of the studied crops, especially of the crop calendar events of herbaceous (i.e., corn, rice, sunflower, and tomato) and woody crops (i.e., almond, walnut, and vineyard), of the NDVI derivatives of rice and the other studied herbaceous crops, and of the NDVI integrals of vineyard and the other studied woody crops. The resulting maps and tables provide valuable geospatial information for every parcel over time with several applications in cropland management, irrigation scheduling, and ecosystem modeling.

MOBILISASI ALSINTAN BERDASARKAN KALENDER TANAM PADA BUDIDAYA PADI DI KABUPATEN GROBOGAN, JAWA TENGAH

<p>ABSTRACT</p><p><br />Mobilization of Agricultural Machines Based on Crop Calender for Rice Cultivation in Grobogan District, Central Java. Agricultural machines for rice production in Central Java especially Grobogan District are already intensively developed, but their utilization is still low. Optimalization use of those agricultural machines is needed to improve their performances. This research aimed: (i) To arrange mobilization concept of agricultural<br />machines in order to optimize their use for rice cultivation in Grobogan District, and (ii) To analyze the deficit and working capacity of those agricultural machines after optimalization use through their mobilization. This research was focused on hand tractors and power threshers only, and was conducted in Grobogan District, in 2013. Data on lowland area and population of hand tractor and power thresher were collected from agricultural office of Grobogan District and its Sub-districts, meanwhile data dealing with agricultural machine’s performances were collected through interview with agricultural machines owner and UPJA using well structured questioners. The collected data were arranged in the form of table and map, and then analyzed using requirement and mobilization analyses. The results showed that through mobilization scenario of 20% available agricultural machines among sub-district with four different planting times in Grobogan District could improve the machine’s performance and could reduce their deficit. By mobilizing those agricultural machines for rice cultivation in Grobogan District, their deficit could be reduced up to &gt;50%, meanwhile, working capacity of those machines could be increased from &lt; 30 ha/year/unit before mobilization become 35,5 ha/year/unit after mobilization.</p><p><em>Keywords: optimalization, mobilization, agricultural machines, crop calendar, rice</em> </p><p>ABSTRAK</p><p><br />Alsintan untuk budidaya padi di Jawa Tengah khususnya di Kabupaten Grobogan sudah berkembang namun pemanfaatannya masih rendah. Mobilisasi alsintan antar wilayah berdasarkan kalender tanam diharapkan dapat meningkatkan pemanfaatannya yang sekaligus meningkatkan kinerjanya. Tujuan kajian ini adalah: (1) Menyusun konsep mobilisasi alsintan berdasarkan kalender tanam untuk optimalisasi pemanfaatannya pada budidaya padi di Kabupaten Grobogan, dan (2) Menganalisis kekurangan dan kapasitas kerja alsintan setelah dilakukan mobilisasi.<br />Kajian ini difokuskan kepada traktor tangan dan perontok padi di Kabupaten Grobogan pada tahun 2013. Data luas lahan sawah serta penyebaran traktor tangan dan perontok padi diperoleh dari kantor Dinas Pertanian Tanaman Pangan Kabupaten dan Kecamatan, sedangkan data primer kinerja alsintan dan jasa sewanya diperoleh melalui wawancara kepada pemilik alsintan dan UPJA di tiga kecamatan yang banyak alsintannya masing-masing tiga responden menggunakan daftar pertanyaan terstruktur. Data yang diperoleh disusun dalam bentuk tabel dan peta, kemudian dianalisis menggunakan Analisis Kebutuhan Alsintan serta Analisis Mobilisasi Alsintan dan Analisis Kapasitas Kerja Alsintan. Konsep mobilisasi alsintan disusun berdasarkan perbedaan jadwal tanam menurut kalender tanam antar kecamatan. Hasil kajian menunjukkan bahwa melalui skenario mobilisasi 20% alsintan yang ada antar kecamatan dengan 4 jadwal tanam padi berbeda di kabupaten Grobogan dapat meningkatkan pemanfaatan dan kinerja alsintan serta menekan kekurangan alsintannya. Dengan skenario mobilisasi alsintan tersebut, kekurangan traktor tangan dan perontok padi di Kabupaten Grobogan dapat ditekan sampai &gt; 50%, sedangkan kapasitas kerja alsintannya dapat ditingkatkan dari awalnya &lt; 30 ha/tahun/unit menjadi 35,5 ha/tahun/unit setelah mobilisasi.</p><p><em>Kata kunci: optimalisasi, mobilisasi, alsintan, kalender tanam, padi</em></p>

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.