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AWD Solution Document: Tanzania

This document provides an overview of the Morogoro and Mbeya regions in Tanzania, focusing on key metrics for irrigation practices, fertilizer use, and burning patterns . It also outlines a plan for baseline assessment and continuous monitoring, utilizing multiple data collection methods to ensure comprehensive insights.

Current Context

Prevailing practice is continuous flooding as well as indiscriminate application of synthetic fertilizers and burning rice straw residue

Tanzania Overview

Tanzania is located on the East Coast of Africa between latitudes 1°S and 11°S and longitudes 29°E and 40°E. It shares borders with Kenya to the north and has total land area of approx. 945,087 square kms.

Climate

Tanzania experiences a tropical climate characterized by seasonal rainfall patterns. Average annual rainfall varies significantly across regions but generally falls between 600 mm to 1,200 mm.

The northern regions experience bimodal rainfall (long rains from March to May and short rains from October to December).

The southern regions including the districts of Mbeya and Morrogoro typically have a single wet season from November to April. Temperatures can drop below 15°C in higher altitudes during the cooler months from June to August.

Agriculture

Agriculture is a vital sector in Tanzania, employing about 65% of the population and contributing nearly 28% to the country's GDP. The nation is known for its diverse crop production, including staple crops like maize, rice, cassava, and sorghum, alongside cash crops such as coffee, cotton, tea, and tobacco.

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Metrics Overview

The following metrics will be analyzed in depth to gain a comprehensive understanding of cropping patterns, irrigation practices, fertilizer use, and burning patterns across Mbeya and Morogoro

Analysed Metrics

Analysed Metrics

Metric

Initial analysis will be conducted through open-source satellite imagery, followed by detailed insights using high-resolution, premium satellite data depending on the project and budget constraints

Historical Analysis

Vegetation Health and Cropping Patterns

NDVI

NDVI is calculated using satellite imagery, which captures data across different wavelengths. The index is particularly sensitive to changes in vegetation cover and can be used to assess various stages of crop growth, making it a valuable tool for monitoring agricultural health. Historical analysis of NDVI can be performed by collecting satellite imagery over multiple growing seasons. By comparing NDVI values across these time periods, trends in vegetation health can be identified, allowing for the assessment of long-term changes due to factors like climate variability, land use changes, or agricultural practices.

Applications

Crop Health Monitoring: NDVI is used to regularly assess the health of rice crops throughout their growth cycle. By analyzing NDVI values over time, we can detect stress or deficiencies early.

Yield Prediction: By correlating NDVI data we conduct historical analysis of agricultural yield using LAI. This is further used to estimate potential future yields, establish baseline and make informed decisions regarding harvesting.

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Irrigation Patterns

Flooding and Drying Days

MNDWI

MNDWI is derived from satellite imagery that captures these specific wavelengths, providing insights into surface water dynamics over time. Historical analysis using MNDWI involves examining changes in water bodies over multiple growing seasons. By comparing MNDWI values year-on-year, trends in water availability and distribution can be established, aiding in understanding long-term impacts on rice irrigation practices.

Applications

Flooding and Drying Assessment: MNDWI is used to track surface water changes in rice fields, and irrigation patterns practiced over time

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MNDWI analysis on 17/08/2023 of sample field in West Bengal, India

MNDWI analysis on 17/08/2023 of sample field in West Bengal, India

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DpRVIc

This metric leverages radar data providing consistent coverage regardless of weather conditions or time of day. Historical analysis using DpRVIc involves examining changes in backscatter signals over multiple growing seasons. By comparing dual-polarization data collected at different times across years or seasons, we will identify irrigation patterns practiced over time

Applications

Flooding and Drying Assessment: Based on SAR data, DpRVIc provides continuous monitoring of surface water changes in rice fields in conjunction with MNDWI

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DpRVIc analysis on 17/08/2023 of sample field in West Bengal, India

DpRVIc analysis on 17/08/2023 of sample field in West Bengal, India

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Soil Moisture

NDMI

NDMI is calculated using optical satellite imagery that captures these specific wavelengths at regular intervals. This allows for continuous monitoring of soil moisture levels over time. Historical analysis using NDMI involves evaluating moisture trends across multiple growing seasons. By comparing NDMI values annually or seasonally, we will assess how moisture availability has changed over time due to climate variations or shifts in irrigation practices.

Applications

Drought Monitoring: NDMI helps identify drought stress in rice crops by assessing moisture levels during critical growth periods, enabling timely irrigation interventions.

Soil Moisture Assessment: The index provides insights into soil moisture conditions indirectly through plant health monitoring, aiding in soil management practices.

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NDMI analysis on 17/08/2023 of sample field in West Bengal, India

NDMI analysis on 17/08/2023 of sample field in West Bengal, India

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Relative Water Level Changes

DInSAR

This method is particularly useful for monitoring agricultural practices such as irrigation management or land preparation for rice cultivation. Historical analysis using DInSAR involves examining deformation patterns over extended periods. By comparing radar images collected at different times across multiple years or seasons, we will identify trends related to relative water level changes at a field level

Applications

Relative Changes in Water Height: Correlating radar images across time for the same field/region, this metric helps to track relative changes in water level in agricultural fields

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DInSAR analysis on 22/10/2023 of sample field in West Bengal, India

DInSAR analysis on 22/10/2023 of sample field in West Bengal, India

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Fertilizer Practices

CNI

This metric leverages remote sensing to capture spectral data across various wavelengths at regular intervals throughout the growing season for evaluating chlorophyll and nitrogen content trends over multiple growing seasons. By comparing CNI values year-on-year or seasonally across different environmental conditions allows understanding long-term impacts on crop health influenced by factors like fertilizer and nutrient application rates.

Applications

Nitrogen Status Assessment: CNI helps evaluate rice crops' nutritional status by measuring chlorophyll levels directly related to nitrogen availability and overall health.

Fertilizer Optimization: By monitoring nutrition content throughout the growing season; we can provide insights into fertilizer application rates, ensuring that crops receive optimal nutrition without over-fertilization.

NDRE

The index leverages satellite imagery that captures these specific wavelengths at regular intervals throughout crop development cycles for continuous monitoring of plant health dynamics over time. Historical analysis using NDRE involves evaluating red edge reflectance trends across multiple growing seasons; this will allow us to identify long-term changes related specifically towards chlorophyll concentrations which correlate closely with overall plant vigor influencing yield outcomes significantly.

Applications

Nitrogen Assessment: NDRE is particularly effective in assessing nitrogen levels in rice crops as it can detect changes in chlorophyll content, which correlates with the nitrogen status of the plants.

Fertilizer Mapping: When it comes to nitrogen fertilizer application, NDRE imagery provides the most accurate picture for precision application of fertilizers and other inputs, optimizing resource use.

Burning Practices

dNBR

dNBR compares pre-and post-event imagery captured via remote sensing techniques specifically designed for assessing burn severity resulting from wildfires or controlled burns within agricultural landscapes. It analyzes differences between normalized burn ratios calculated using NIR reflectance before fire events versus after assessing impacts on vegetation cover post-disturbance effectively quantifying recovery rates over time following disturbances.

Applications

Burn Severity Assessment: dNBR provides valuable insights into how severe a fire was affecting vegetation cover; helping guide recovery efforts post-fire events ensuring sustainable land management practices are implemented effectively.

Vegetation Recovery Monitoring: By tracking dNBR values over time post-fire; we gain insights into recovery rates allowing them to adaptively manage resources aimed at restoring affected areas efficiently.

MODIS Burned Area

The MODIS Burned Area product utilizes satellite imagery designed specifically for identifying areas affected by fire through spectral analysis across multiple bands focusing primarily upon detecting changes associated with burn extent severity over time providing valuable insights regarding impacts resulting from wildfires and controlled burns agricultural practices alike.

Applications

Monitoring Burned Areas: MODIS provides timely assessments regarding burned area extents allowing stakeholders to understand immediate impacts resulting in fire events facilitating adaptive management strategies

Impact Assessment On Soil Quality: Understanding how fires affect soil quality informs subsequent cropping decisions ensuring sustainable practices are implemented effectively minimizing adverse effects

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MODIS Burn analysis for 2023 of sample region in Madhya Pradesh, India

MODIS Burn analysis for 2023 of sample region in Madhya Pradesh, India

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Feasibility Analysis

OnePoint5’s proprietary ML models and data analytics engine offer an optimized framework for identifying suitable regions within Mbeya and Morogoro for the AWD project, in accordance with the

Gold Standard methodology⁠

to ensure precise selection criteria are met, facilitating compliance with established sustainability standards and enhancing project viability.

Gold Standard Applicability

Gold Standard Applicability

Section

Applicability

OnePoint5's Analysis

2.2.1 a)

Rice cultivation method: Only irrigated, flooded rice fields are eligible for the project. Fields that are upland, rainfed, or deep water types are excluded. This must be proven by a regional survey or national data, covering aspects like water regime and organic amendments.

Irrigation pattern analysis will help separate out the fields that are classified under the upland, rainfed, or deep water regime and thereby only controlled irrigation regions will be mapped across Mbeya and Morogoro

2.2.1 b)

Irrigation control: Rice fields must have controlled irrigation and drainage systems to maintain proper dry/flooded conditions throughout the year.

Historical Irrigation analysis will be utilised to map out the fields with controlled irrigation and drainage system

2.2.1 c)

Rice yield preservation: The project must ensure that the new practices do not reduce rice yield.

Leaf Area Index (LAI) and NDVI measurements will be used to establish the consistency in rice yield

2.2.1 d)

New rice cultivars: If introducing a new rice variety, it must not require changes in existing land management practices.

This is not in our current scope as we are working under the assumption that no new rice variety is being introduced

There are no rows in this table

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Baseline Analysis

The baseline analysis will establish the pre-project conditions in the target areas and similar regions across different parts of Southern Tanzania:

a) Area Selection:

OnePoint5 will establish a baseline using their AWD module consisting of fine-tuned ML models coupled with the Geospatial analysis to detect regions that have similar cultivation and irrigation practices along with climatic conditions, thus providing a reliable comparison point for evaluating project impacts.

b) Pre-project Conditions:

Analyze NDVI, MNDWI and NDMI trends in selected areas to establish baseline vegetation health and cropping patterns

Analyze MNDWI, DpRVIc, NDMI and DInSAR trends in selected areas to establish irrigation patterns

Use NDRE and CNI to assess baseline fertilizer practices

Analyze dNBR and MODIS Burned Area data to understand baseline burning practices

c) Leaf Area Index (LAI):

We will analyze LAI for baseline and project area before and after the practice to verify that there is no effect on yield of rice production with AWD

Carbon Sequestration Estimates

Based on GS guidelines, taking into account existing irrigation parameters, fertilizer usage and CH4 emissions factor prescribed, we can provide estimates for potential carbon sequestration per cropping cycle.

Monitoring Plan

The monitoring plan for the AWD project will focus on tracking key indicators related to vegetation health, irrigation practices, fertilizer usage, and yield outcomes.

Vegetation Health and Yield: NDVI will be used to assess the health and density of rice crops. Regular monitoring will provide insights into crop performance over time.

Irrigation Patterns: A combination of MNDWI, DpRVIc, NDMI and DInSAR will be used to monitor soil moisture, flooding, and drying days, which are critical for implementing AWD practices.

Fertilizer Practices: NDRE and CNI will be utilized to assess the nitrogen sensitivity of crops, indicating nitrogen-based fertilizer use and providing insights into nutrient management practices.

Burning Patterns: To assess the frequency and severity of burning events, dNBR and MODIS Burned Area will be used to evaluate the impact on soil health and cropping patterns.

Monitoring Frequency:

Irrigation Classification: We will conduct weekly monitoring during critical rice growth stages.

Fertilizer: We will perform bi-weekly monitoring, with increased frequency around expected rice fertilization events.

Stubble Burning: We will implement daily monitoring immediately after the rice harvest season.

For example, please refer the optimized sample satellite monitoring plan from our previous project,

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Optimized Schedule Plans - Monitoring & Reporting Assessment & Recommendations.png

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Testing and Data Collection

OnePoint5 ensures cost-effective monitoring by utilizing only a few paid satellite images for validation when budget constraints arise. To enhance accuracy and provide a higher level of detail, we can collaborate with our drone analysis partners further improving the precision of remote sensing

Data Collection Methods

Data Collection Methods

Method

Advantages

Limitations

Insights

Open Source Satellites

Resolution: Monitors 'Water presence on Surface' and 'Soil Moisture' (20m) ; 'Change in Water Height' and 'Vegetation Health' (10m)

Inexpensive: Easy to set up, cost-effective

Frequency Constraints: Limited availability on certain days

Cloud Cover: Parameter monitoring is affected by cloud cover, hindering accuracy

Ideal for large-scale applications, offering a cost-effective layer for comprehensive monitoring.

Paid Satellites

High Resolution: Offers imagery up to 1m, ideal for monitoring small rice plots.

Broad Coverage: Covers large areas, providing extensive data for regional water management.

Cost: Expensive for large-scale or continuous monitoring; costs range from $2.25 to $30 per 100 hectares for one-day usage.

Best for occasional use to improve accuracy in small-scale AWD monitoring and establishing granular baselines

IoT Devices and Pipeline

Continuous Monitoring: Enables real-time, 24/7 tracking of soil moisture and water levels.

Low Maintenance: Minimal upkeep after installation; systems can last up to five years.

High Initial Costs: Significant upfront investment for setup and equipment.

Infrastructure Needs: Requires robust infrastructure for effective data pipelines

More effective for precision agriculture and continuous monitoring in smaller AWD projects.

Best when combined with more affordable solutions to achieve broader coverage.

Drones

High Resolution: Provides detailed data at sub-centimeter levels.

Access to Inaccessible Areas: Can reach small or hard-to-access locations effectively.

Cost: Drone shots are generally expensive, OnePoint5 optimises the area to be sampled to further reduce costs

Ideal for small to medium-sized AWD projects needing detailed assessments.

Best suited for occasional monitoring but complements satellite data in challenging areas.

Sampling with Offline Data Collection

High Accuracy: Achieves precise measurements through physical, on-the-ground data collection.

Flexible Deployment: Allows targeted sampling based on specific project needs.

Labor-intensive: Requires manual data collection, which can be time-consuming.

No Real-Time Monitoring: Depends on field visits; lacks continuous tracking capabilities

Effective as a complementary data source with automated methods, providing a more comprehensive view of the project.

Mobile App-based Log Book for activity photo + GPS Data

Real-Time Tracking: Logs flooding and irrigation with precise GPS and timestamps.

User-Friendly: Simple interface, adaptable for different farming practices.

Training Required: Needs initial training and ongoing support for farmers.

Device Dependency: Relies on farmers having smartphones with GPS capabilities.

Best for direct farmer engagement and ground-truth data collection.

Complements satellite data for enhanced project monitoring and compliance.

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Conclusion

Our holistic approach considers all essential parameters throughout the lifecycle of the AWD project. By leveraging data from a variety of sources, we ensure our results are as accurate and close to the ground truth as possible. With our finely-tuned machine learning models and user-friendly data insights, we provide a comprehensive understanding that empowers informed decision-making to assess the feasibility and execution of the project.

Dashboard

Our dashboards provide real-time data analysis, making it easy for users to track important metrics. The user-friendly interface allows quick access to key information, easy navigation, and sharing with stakeholders. With advanced analytics, we offer useful insights that improve transparency and help with better decision-making for sustainability goals.

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