Chief Executive Officer

Trainer

Directeur de recherche - Research Director

CESSMA

<p></p><p>📖 About the Project This Power BI project presents a comprehensive and interactive analysis of an agricultural survey conducted with 50 farmers distributed across 5 delegations in the Monastir region: Zéramdin, Menzel Kamel, Touza, Jemmal, and Bébalita. The main objective is to apply precision agriculture principles by analyzing farming practices, resource utilization, and phytosanitary challenges. 🎯 Problem Statement How can we optimize agricultural production in the Monastir region by identifying the most effective practices, irrigation needs, and phytosanitary threats? This analysis enables data-driven decision-making to improve productivity and sustainability.</p><p>🏗️ Technical Architecture Star Schema Data Model The data model follows a star schema architecture with the PARCELLE (Plot) table as the central fact table (the "dynamo" of the model). This design optimizes performance and facilitates multidimensional analysis.</p><p>Primary join key: ID_PARCELLE + DELEGATION This architecture enables:</p><p>✅ Efficient cross-filtering between all dimensions ✅ Fast aggregations by delegation, farmer, or plot ✅ Consistent temporal analysis ✅ Scalability to add new dimensions</p><p>🗂️ Report Structure (11 Interactive Pages) #PageDescriptionKey Visualizations1SummaryCentral navigation hub with images of 2 surveyorsNavigation buttons, dynamic images2Farmer ContactDemographic profiles (age, education, property type)Geographic map, age charts, KPIs3Olive ProductionAnalysis of olive trees (old/new, uprooted)Temporal charts, detailed table4General ProductionCrop types by delegationBar charts, distribution5Irrigation50 irrigated plots, types (local 92% / social 8%)Temporal evolution, techniques (manual/electric/solar)6Product UsageAgricultural equipment (4 herbicides, 6 soil analyzers)Inventory, seasonal usage7Soil WorkTillage tools (Canadian, moldboard, disc plows)Detailed table by plot and season8Olive Tree PruningPruning practices, interventions, paymentsIntervention analysis by period9Pests &amp; Diseases12 diseases (wet) vs 47 diseases (dry season)Tables by delegation and type10HarvestTransportation methods, self-consumption, durationFinancial and logistics data11ExpensesOlive purchases and expenses by seasonFinancial tracking by plot</p><p>🔬 Methodology: Precision Agriculture This project applies precision agriculture principles through:</p><p>Exact Geolocation: Interactive mapping of plots by delegation Granular Analysis: Plot-level data (ID_PARCELLE) Temporal Monitoring: Practice tracking by season (wet/dry) Data-Driven Decisions: Real-time KPIs to optimize resources</p><p>📊 Key Performance Indicators (KPIs)</p><p>50 agricultural farmers 50+ irrigated plots 10 farmers per delegation (average) 54.6 years average age 92% local irrigation vs 8% social 78% "older" farmers vs 22% "young"</p><p>🛠️ Technologies &amp; Tools ToolUsageWhy This Choice?Power BI DesktopReport creation, visualizationsIntuitive interface, native interactivitySQL ServerData cleaning and transformationMore dynamic than Power Query for massive UPDATEsDAXCustom calculated measuresComplex aggregations and time intelligencePower Query (M)Complementary transformationsSource connections and restructuring</p><p>🧹 Data Processing: Advanced SQL Cleaning Why SQL Over Power Query? For this project, SQL Server was preferred for data cleaning because:</p><p>✅ Faster for UPDATEs on large tables ✅ More flexible with complex CASE WHEN statements ✅ Traceability: Reusable and versioned scripts ✅ Standardization: Value uniformization (e.g., plow types)</p><p>SQL Script Example sql-- Standardization of tillage tools in wet season UPDATE [Enquete].[dbo].[la-taille-olivier] SET [Outil_de_labour_en_année_humide] = CASE WHEN ID_PARCELLE = 'IDPAR002' THEN 'Charue à soc' WHEN ID_PARCELLE = 'IDPAR005' THEN 'Charrue canadienne' WHEN ID_PARCELLE = 'IDPAR011' THEN 'Charrue canadienne' WHEN ID_PARCELLE = 'IDPAR016' THEN 'Charue à soc' WHEN ID_PARCELLE = 'IDPAR017' THEN 'Charue à soc' WHEN ID_PARCELLE = 'IDPAR019' THEN 'Charue à soc' WHEN ID_PARCELLE = 'IDPAR020' THEN 'Charue à soc' WHEN ID_PARCELLE = 'IDPAR021' THEN 'Charue à soc' WHEN ID_PARCELLE = 'IDPAR028' THEN 'Charrue canadienne' WHEN ID_PARCELLE = 'IDPAR030' THEN 'Charue à disque' ELSE [Outil_de_labour_en_année_humide] END WHERE ID_PARCELLE IN ( 'IDPAR002', 'IDPAR005', 'IDPAR011', 'IDPAR016', 'IDPAR017', 'IDPAR019', 'IDPAR020', 'IDPAR021', 'IDPAR028', 'IDPAR030' );</p><p>-- Result: Standardized values for 3 plow types -- Canadian (7), Moldboard (7), Disc (8)</p><p>🎨 Advanced Features</p><ol><li><p>Dynamic Surveyor Images</p></li></ol><p>Photos of 2 surveyors that change based on selected delegation Creates a human connection with the data</p><ol start="2"><li><p>Intuitive Navigation</p></li></ol><p>Home page (Summary) with buttons to all sections Return to summary available from each page</p><ol start="3"><li><p>Interactive Maps</p></li></ol><p>Precise geolocation of farmers by delegation Location of plots 1 and 2 with additional data</p><ol start="4"><li><p>Dynamic Filters</p></li></ol><p>By delegation (5 zones) By acquisition year By crop type Cross-filtering across all pages</p><ol start="5"><li><p>Comparative Analysis</p></li></ol><p>Old vs new olive trees (temporal evolution) Wet vs dry season (diseases, tools) Local vs social irrigation</p><p>📈 Key Insights &amp; Recommendations 🔍 Major Findings</p><p>Aging Agricultural Population</p><p>78% of farmers are over 50 years old Recommended Action: Training programs for young farmers</p><p>Local Irrigation Dominance</p><p>92% local irrigation vs 8% social Recommended Action: Study access to collective irrigation networks</p><p>Increased Phytosanitary Pressure in Dry Season</p><p>47 diseases in dry season vs 12 in wet season Recommended Action: Strengthen monitoring and preventive treatments</p><p>Concentration of Acquisitions</p><p>Majority of plots acquired between 2000-2019 Insight: Recent agricultural investments, modernization opportunity</p><p>Diversity of Tillage Equipment</p><p>3 types of plows used equally Insight: Adaptation to soil types and crops</p><div class="image-container"><div class="image-row-wrapper"><div class="image-wrapper"><img src="https://cdn.collective.work/cmu1oqs091ez952kf03h96ztj.png"><div class="image-overlay"></div></div></div></div><div class="image-container"><div class="image-row-wrapper"><div class="image-wrapper"><img src="https://cdn.collective.work/cmu1oppob1c824kih8l4bht7p.png"><div class="image-overlay"></div></div></div></div>

Akram Brahem

Microsoft Certified: PL-300 | Data Analyst |

Recommandé par

À propos

Expérience de travail

data analyste

Chief Executive Officer

Formation

Gate Training

GOMYCODE

Ecole internatianale de management de sousse

Université privée de Sousse

Licences et certifications

Microsoft Certified: Power BI Data Analyst Associate (PL-300)

Portfolio

Enquete Agronomique

CESSMA

Talents

Recruteurs

À propos