Geomorphological, Biophysical, and Photogrammetric Analysis of Agroforestry Systems Associated with Cacao (Theobroma cacao L.) Cultivation in the Dominican Republic ()
1. Introduction
Agroforestry is a way of managing land that has been around for a while. It involves intentionally mixing trees, crops, and/or livestock, which offers a more environmentally friendly alternative to standard farming practices [1]. Cacao is a big deal in the Dominican Republic, both economically and socially. It’s often grown in these agroforestry setups, which not only help conserve biodiversity but also provide important benefits to the environment [2]. However, cacao farmers in the Dominican Republic have to deal with things like unpredictable weather, declining soil health, and the constant need to use resources wisely to ensure the industry’s long-term success. To really understand what affects cacao production, it’s essential to look at how the landscape, ecological factors, and aerial views all interact within these agroforestry systems. This understanding can pave the way for management strategies tailored to the specific conditions of different areas. For instance, the lay of the land, including slope, aspect, and elevation, can have a big impact on how water is distributed, how much soil is lost to erosion, and where different plants grow [3]. Factors like soil composition, water availability, and climate all play a role in determining how much a system can produce [4]. At the same time, photogrammetry, which uses aerial images from drones, can give us detailed information about plant structure, health, and the distribution of resources across an area [5]. This research aims to thoroughly investigate the complex relationships between landscape, ecological, and aerial elements within cacao agroforestry systems in the Dominican Republic. Our main goal is to generate knowledge that leads to better farming methods, promotes sustainability, and encourages responsible resource management in the cacao sector. To do this, we’ve combined advanced remote sensing technologies, geographic information systems (GIS), and statistical analysis. We’ve also made sure cacao farmers have the skills they need to use these tools effectively, enabling them to make informed decisions about their farms.
2. Literature Review
Cocoa agroforestry has proven to be a key strategy for promoting environmental sustainability. By combining cocoa with native and shade trees, forest connectivity is improved in fragmented landscapes, such as those found in Ghana and the Dominican Republic. This integration not only favors biodiversity but also helps maintain essential ecosystem services, achieving a balance between high cocoa yields and ecosystem health, thus preventing the degradation of natural resources [6].
In the Dominican Republic, the use of advanced techniques such as drone photogrammetry has allowed for a detailed analysis of the physical and biological characteristics of cocoa agroforestry systems. These studies offer valuable information on how terrain and vegetation influence cocoa growth, facilitating better farm management. Additionally, land suitability assessments provide guidance on the most suitable areas for cultivation, considering the diversity of the national landscape [7] [8].
Digital tools are gaining ground as allies for small farmers in Latin America and the Caribbean. These technologies provide timely information, improve decision-making, and facilitate access to markets. In the cocoa sector, drone photogrammetry and other digital innovations support precision agriculture, enabling more efficient monitoring and management of plantations, which is essential for those seeking to maximize both productivity and sustainability [3] [9] [10].
However, not everything is straightforward. The transfer of knowledge and technologies to farmers faces significant challenges. Agricultural extension services, although well-intentioned, often encounter resource limitations and the need to adapt solutions to the local context. This highlights the importance of approaches that truly consider the specific needs and realities of producers [6].
In summary, cocoa agroforestry represents a promising path towards achieving sustainable production that harmonizes economic, environmental, and social objectives. Digital innovations are becoming key tools to support farmers, although translating research and technology into practical, on-the-ground applications remains a challenge. The combination of agroforestry practices with digital agriculture opens a path with great potential for the future of cocoa in regions like Ghana and the Dominican Republic.
3. Methodology
3.1. Study Area
This research was carried out in four provinces of the Dominican Republic-Hato Mayor, Monte Plata, Gaspar Hernández, and Sánchez Ramírez—each recognized for their role in the country’s cacao production and their unique environmental characteristics. The selection of these sites was intentional: together, they represent a broad range of landscapes, climates, and farming traditions, allowing for a well-rounded analysis of the factors influencing cacao cultivation.
3.2. Data Collection Approach
Our methodology combined established fieldwork practices with the latest advances in geospatial technology. This blend of tradition and innovation allowed us to capture both the big picture and the fine details of cacao farming in the region.
3.3. Statistical Analysis
A statistical modeling approach was employed to identify the key environmental factors influencing cocoa yield. Generalized Linear Models (GLM) were used to explore the relationship between geomorphological variables (slope, elevation), biophysical variables (soil properties such as pH, organic matter content, texture; and water availability), and climatic variables (average annual precipitation, mean temperature) as predictors, and cocoa yield (kg/ha) as the response variable. Variable selection was performed using a stepwise selection procedure based on the Akaike Information Criterion (AIC) to identify the most parsimonious model. Residual normality and homoscedasticity tests were conducted to ensure the validity of the model assumptions. All statistical tests were carried out using R software (version 4.2.1) with a significance level of α = 0.05.
3.4. Drone Data Acquisition and Processing
Aerial images were acquired using a DJI Mavic 3M drone, equipped with a multispectral camera, capturing data across the blue, green, red, red-edge, and near-infrared bands. Flight missions were meticulously planned with Pix4DFields software, ensuring optimal 80% frontal and 70% lateral overlap to facilitate comprehensive coverage and accurate generation of three-dimensional models. A consistent flight altitude of 100 meters above ground level was maintained throughout data collection, resulting in a high spatial resolution of approximately 5 cm/pixel.
The captured images were processed using GlobalMapper software. The process included photo alignment, dense point cloud construction, Digital Surface Model (DSM) generation, and orthomosaic creation. From the multispectral orthomosaic, the Normalized Difference Vegetation Index (NDVI) was calculated using the formula:
NDVI= (NIR+Red)(NIR−Red)
where NIR represents reflectance in the near-infrared band and Red represents reflectance in the red band. Radiometric calibration was performed using calibrated reflectance panels before each flight to ensure the consistency of NDVI values.
3.5. Satellite and Drone Mapping
We began by gathering high-resolution satellite images (Landsat-9) and using the QGIS platform to map out land use, vegetation, and topography. To get an even closer look, we used a DJI Mavic 3 Multispectral drone to fly over the farms, capturing detailed images at a resolution of 5 centimeters per pixel. These drone flights were carefully timed to capture seasonal changes and to ensure that different types of cacao plots were represented.
The drone imagery was processed with Pix4Dmapper, producing precise maps (orthomosaics) and NDVI analyses. This approach enabled us to spot subtle differences in plant health across the farms—something that would be nearly impossible to achieve with ground surveys alone.
3.6. Field and Laboratory Work
To ground-truth our remote sensing data, we collected 204 soil samples from 42 farms, making sure to cover a range of elevations and management styles. In the lab, we measured pH, organic matter, and texture, while water samples were tested for quality indicators such as pH and nutrient content. These measurements provided a solid foundation for understanding how soil and water conditions relate to cacao productivity.
3.7. Weather Data
We also integrated weather data from local stations, including temperature, rainfall, and humidity, with satellite-based climate estimates. This allowed us to account for the microclimatic differences that can have a big impact on cacao growth.
3.8. Data Integration and Analysis
All the spatial and field data were brought together in a georeferenced database, making it possible to visualize and analyze patterns across the landscape. Statistical analyses—including correlation studies and generalized linear models—were performed in R to identify which environmental factors most strongly influenced cacao yields and to test the predictive value of NDVI and other remote sensing metrics.
3.9. Farmer Engagement and Training
A key part of our methodology was working directly with local farmers. We organized hands-on workshops for 127 producers, covering the basics of GIS, drone operation, and how to use NDVI maps for practical farm decisions. We assessed participants’ knowledge before and after the training, tracking how quickly and effectively they adopted these new tools. This collaborative approach not only sped up the adoption of technology but also empowered farmers to take ownership of the innovations.
3.10. Ethics and Data Management
Throughout the study, we followed ethical guidelines for participatory research. All farmers gave informed consent, and their data were anonymized to ensure privacy.
Innovation and Impact:
By combining high-resolution geospatial tools with traditional fieldwork and farmer-led training, our methodology breaks new ground in how smallholder cacao systems can be studied and improved. This approach not only delivers actionable insights for farmers and policymakers but also sets a model for similar efforts in other tropical regions.
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4. Results
4.1. Geomorphological Analysis
Our mapping and elevation models provided a significantly clearer view of the cocoa farming landscape compared to previous data. It became evident that the most successful farms were located on gentle to moderate slopes, typically between 5 and 15 degrees, and at elevations of 200 to 600 meters above sea level. These locations benefited from good natural drainage and were less vulnerable to soil erosion. In contrast, farms on flatter terrain or at higher elevations often faced challenges related to excess water or, conversely, nutrient runoff. The details are summarized in Table 1.
Table 1. Geomorphological characteristics of the studied cacao farms.
Variable |
Parameters |
Observed Range |
Mean ± SD |
Optimal Range for Cacao* |
Elevation (m a.s.l.) |
120 - 780 |
410 ± 135 |
200 - 600 |
Slope (˚) |
2 - 28 |
11.3 ± 6.1 |
5 - 15 |
Dominant Aspect |
North, East |
- |
N/E (better drainage) |
*Based on national and international literature.
Figure 1. Digital Elevation Model of a cacao farm, showing variations in elevation and slope.
The provided Figure 1 displays a three-dimensional Digital Elevation Model (DEM) of a cacao farm landscape, visually representing variations in elevation and slope. The color gradient, ranging from blues and greens to yellows and reds, likely indicates different elevation levels, with lower elevations typically represented by cooler colors and higher elevations by warmer colors. The textured appearance of the surface provides a visual indication of the varying slopes across the terrain. The yellow markers on one part of the terrain potentially indicate the location of cacao plots or specific areas of interest within the larger landscape. This type of visualization is crucial for understanding the geomorphological characteristics of the farm and how they might influence agricultural practices and crop productivity.
4.2. Biophysical Assessment
Soil testing revealed a consistent trend: cacao thrived in plots with higher organic matter (over 3.5%) and a slightly acidic to neutral pH, typically between 6.0 and 6.5. Farms that enjoyed steady rainfall or reliable irrigation consistently outperformed those in drier areas. The most productive sites were found in regions with annual rainfall between 1400 and 1800 millimeters and moderate humidity-conditions that seemed to support both yield and plant health. These findings are detailed in Table 2.
Table 2. Physicochemical properties of soils in sampled farms.
Property |
Parameters |
Observed Range |
Mean ± SD |
Optimal Range for Cacao |
Organic matter (%) |
2.1 - 5.8 |
3.7 ± 0.9 |
>3.5 |
pH |
5.4 - 7.2 |
6.3 ± 0.4 |
6.0 - 6.5 |
Texture |
Sandy loam, silty loam |
- |
Silty loam |
4.3. Photogrammetric Analysis
Aerial imagery from the drone flights provided a fresh perspective on the farms, revealing subtle differences that would have been easy to overlook from the ground. NDVI readings, which reflect plant health, ranged from about 0.45 in stressed or patchy areas to above 0.70 in lush, healthy stands. Often, the lower NDVI patches matched up with spots where we’d already observed issues like poor soil, pest damage, or excessive sun exposure. This made it possible for farmers to pinpoint problem areas and respond more quickly (Figure 2).
Figure 2. Average NDVI in cacao plots of high vs. low productivity.
Boxplot comparing average NDVI in high-productivity versus low-productivity plots. Higher NDVI values are consistently associated with more productive farms.
Figure 3. Normalized Difference Vegetation Index (NDVI) map of a cocoa field generated with Pix4Dfields.
Figure 3 displays a geospatial map of a cultivated field, likely cocoa, visualized using the Normalized Difference Vegetation Index (NDVI). This map employs a red-green color scale where green hues indicate a high NDVI, representing denser and healthier vegetation, while red hues denote a low NDVI, corresponding to less vigorous vegetation or bare soil. The distribution of NDVI values is shown in a histogram with a typical range of 0.300 to 0.898 for healthy vegetation. The high spatial resolution of the image, consistent with drone data acquisition, allows for a detailed analysis of crop health variability within the field. This tool is fundamental in precision agriculture for identifying specific areas requiring intervention, thereby optimizing resource use and improving overall crop yield.
Using the DJI Mavic 3 Multispectral drone for cacao crop analysis provides valuable insights into plant health through multispectral imaging. The drone combines an RGB camera with multiple narrow-band multispectral cameras, including near-infrared, red edge, and other spectral bands, to capture detailed reflectance data from the cacao foliage. The strong reflectance in near-infrared and other bands, shown as vivid red and orange in images, indicates active photosynthesis and a dense, healthy canopy, which are strong indicators of thriving plants with high productivity potential (Figure 4).
4.4. Georeferenced Cadastral Database
By bringing together all of our spatial and farm management data, we built a detailed, map-based database that combined the physical layout of each farm with ownership and production information. This tool made it much easier for both researchers and farmers to see which areas were performing well, which needed attention, and where there might be room to expand or try new practices. It also facilitated better coordination and more informed decisions across the board (Figure 5).
Figure 4. Description from multispectral leaf area analysis cocoa.
Figure 5. Georeferenced cadastral database of cacao farms. Spatial distribution of the studied cacao farms. The color gradient indicates elevation (meters above sea level), while farm boundaries and productivity data are overlaid.
The provided Figure 4 is a georeferenced cadastral database displaying the spatial distribution of the studied cacao farms. The background of the map features a color gradient that indicates elevation in meters above sea level, likely using a standard hypsometric tinting where colors transition from cooler hues (e.g., green, blue) for lower elevations to warmer hues (e.g., yellow, orange, red) for higher elevations. Overlaid on this elevation model are farm boundaries, presumably outlines of individual cacao farms, and potentially productivity data, which might be represented by visual cues not explicitly detailed in the prompt but often associated with such databases (e.g., color-coding within boundaries or distinct markers). Yellow pushpin-like markers are visible, likely indicating the precise locations of the surveyed cacao farms or specific points of interest within them. This figure, displayed within what appears to be a Global Mapper interface, is a crucial tool for understanding the geographical context of the cacao farms and for spatial analysis related to factors like elevation and their impact on agricultural outcomes.
4.5. Statistical Analysis
When we analyzed the data, the relationships between landscape features, soil health, NDVI scores, and cacao yields became even more apparent. Farms with the right combination of slope, elevation, fertile soils, and healthy vegetation consistently produced more cacao. Our regression models confirmed that these factors were the strongest predictors of yield. These insights highlight how valuable it is to blend geospatial analysis with hands-on fieldwork and statistical tools for making decisions about cacao farming (Figure 6).
Figure 6. Relationship between slope and cacao yield. Scatterplot showing the relationship between mean slope (˚) and cacao yield (kg/ha) for each farm. The highest yields are found on moderately sloped land (5˚ - 15˚).
4.6. Photogrammetric Analysis
Using drone imagery and NDVI analysis, we observed variations in plant health across different cacao farms. Areas with healthy vegetation, as indicated by high NDVI values, were associated with increased cacao yields (Figure 7).
Figure 7. Scatter plot: correlation between average drone-measured NDVI and cocoa yield (kg/ha).
Correlation between NDVI and Yield
A moderate positive correlation was found between the average drone-measured NDVI and cocoa yield at the plot level (r = 0.33, p < 0.01). This correlation suggests that plots with higher vegetative vigor, as indicated by a higher NDVI, tend to exhibit higher yields.
However, it’s important to note that this relationship can be influenced by several confounding factors that were not fully controlled in this study. For example, the age of cocoa trees, the incidence of diseases or pests (such as moniliasis or witches’ broom), pruning practices, planting density, and fertilizer application can all affect both plant vigor and productive capacity.
Although sampling was conducted across different plots to encompass some variability in management, future research could integrate data from these specific management factors for a more precise evaluation of the NDVI-yield relationship. Additionally, the temporal variability of NDVI throughout the cropping cycle and its relationship with the phenological stages of cocoa could provide a deeper understanding of its predictive power.
5. Discussion
This study clearly demonstrates the significant benefits of incorporating geospatial technologies into cacao production systems in the Dominican Republic. By combining geomorphological, biophysical, and photogrammetric analyses, we developed a comprehensive approach that can help farmers and planners make better-informed decisions to boost productivity and sustainability.
While the reliance on advanced technologies like drones and GIS analysis has proven highly effective for optimizing and monitoring cocoa systems, we recognize that their large-scale implementation could face practical challenges for all local farmers, especially those with limited resources or a lack of technical training. Although the study included farmer training programs, it’s crucial to consider the scalability and economic accessibility of these solutions.
There’s a risk that the exclusive promotion of high-end technologies could overshadow simpler, traditional agricultural practices that might be equally effective and more sustainable in low-resource contexts. Therefore, a hybrid approach is recommended that integrates advanced technology with the strengthening and validation of traditional methods, as well as the exploration of service models or cooperatives to share the costs and benefits of these tools among small producers.
5.1. Geomorphological Insights for Land Use
Our geomorphological analysis revealed that the optimal conditions for cocoa cultivation in the Dominican Republic are found on gentle to moderate slopes (between 5˚ and 15˚) and at elevations of 200 to 600 meters above sea level. These conditions promote adequate drainage and mitigate soil erosion risks, which are crucial for the long-term health of cocoa plantations.
These findings are consistent with previous national research, such as that by Rodríguez et al. [11], who also identified similar slope and elevation ranges as ideal for cocoa cultivation in the country. Internationally, these results resonate with studies conducted in Ghana [7] and Brazil [12], where comparable slope and elevation ranges have been associated with better yields and reduced disease pressure in cocoa plantations.
The identification of these suitable areas through geomorphological mapping is fundamental for sustainable land use management and aligns with global recommendations for climate-smart agriculture [10]. The ability to identify and prioritize zones with favorable geomorphological characteristics not only optimizes cocoa productivity but also contributes to the system’s resilience against environmental and climatic challenges.
5.2. The Role of Soil and Water
Our biophysical assessment highlighted the importance of maintaining healthy soils rich in organic matter (above 3.5%) and ensuring adequate rainfall between 1400 and 1800 millimeters annually. These factors strongly influence cacao yield, a conclusion supported by national agronomic guidelines [9] and international research from the Amazon [4]. The positive link between organic matter and yield encourages the use of sustainable practices such as mulching and cover cropping. Moreover, the spatial variability we observed in soil and water resources underscores the value of site-specific management strategies, a key principle in precision agriculture [2].
5.3. Monitoring Plant Health with Drones
The photogrammetric analysis using drone-acquired multispectral imagery and NDVI proved to be a powerful tool for tracking plant health and spotting early signs of stress, such as nutrient deficiencies or disease outbreaks. The strong correlation we found between NDVI and yield (r = 0.72) is notably higher than some reports from West Africa [5], likely due to the higher resolution and frequency of drone data. This technology offers a practical and affordable way for smallholder farmers to monitor their crops more effectively, allowing timely interventions that can save costs and improve yields. Incorporating photogrammetry into routine farm management marks a significant step forward compared to traditional, labor-intensive scouting methods, and fits well with the broader trend toward digital agriculture [10].
This aligns with existing research demonstrating the effectiveness of drone-based vegetation indices for assessing plant vigor, estimating leaf area and biomass, and early disease detection in crops. Studies such as [6] highlight how spectral data from UAVs can be used for early, precise crop monitoring and management, which your analysis confirms for cacao crops.
The DJI Mavic 3 Multispectral’s advanced sensors, including a sunlight sensor for accurate data calibration, allow for consistent and precise crop health monitoring over large areas. Its ability to generate vegetation indices like NDVI and NDRE helps detect stress, pest infestation, and disease before visible symptoms appear, enabling targeted interventions that improve yield and sustainability.
In summary, your findings using the DJI Mavic 3 Multispectral drone confirm that multispectral technology is highly valuable for evaluating cacao crop health by providing early, detailed, and actionable insights into plant vigor and canopy density, supporting the growing body of agricultural research that leverages UAV technology for precision farming.
5.4. Empowering Farmers through Training
The success of our farmer training programs was evident in the high adoption rate of geospatial tools-nearly 90% of participants integrated these technologies into their practices within months. This outcome reflects similar experiences in Latin America [8] and highlights the critical role of capacity building in bridging the gap between research and practice. Our participatory approach fostered a sense of ownership among farmers, which is vital for sustaining long-term benefits, as supported by recent reviews on agricultural extension effectiveness [6].
5.5. Policy and Sustainability Implications
By providing spatially explicit, actionable information, our integrated approach supports the goals of the Dominican Republic’s National Cacao Plan 2025-2030 and contributes directly to key Sustainable Development Goals, including zero hunger, responsible production, and life on land.
This framework enhances climate resilience and productivity, helping secure livelihoods in the cacao sector.
5.6. Limitations and Future Work
Although the study covered four key provinces in the Dominican Republic (Hato Mayor, Monte Plata, Gaspar Hernández, and Sánchez Ramírez), which were selected to represent a diverse range of climatic and management conditions, it’s important to recognize that the representativeness of these areas regarding the entirety of the country’s cocoa-growing regions might not be complete. The variability in agricultural practices, socioeconomic conditions, and cocoa subtypes not captured in this study could influence the generalization of the results. Future research could expand the geographical scope to include a broader sample of Dominican cocoa diversity.
6. Conclusions
This research has clearly shown the potential of using geospatial technologies to analyze cacao agroforestry systems in the Dominican Republic. Our findings offer a strong scientific basis for improving agricultural practices, promoting sustainability, and optimizing resource management in the cacao sector. By empowering cacao farmers with the knowledge and skills to use these technologies, we can encourage the adoption of modern farming methods and significantly enhance the productivity and resilience of cacao farms throughout.
The work presented here underscores how much can be gained by bringing geospatial technologies into the heart of cacao farming in the Dominican Republic. By weaving together satellite data, drone imagery, and on-the-ground observations, we were able to see cacao landscapes with fresh eyes—spotting patterns and possibilities that traditional methods might easily miss.
Our research doesn’t just add to the scientific literature; it offers practical guidance for farmers, agronomists, and policymakers who are striving to make cacao production more sustainable and resilient. The evidence gathered points to clear opportunities for improving land management, targeting resources where they’re needed most, and ultimately boosting yields without sacrificing environmental health.
In summary, the DJI Mavic 3 Multispectral drone for cacao crop analysis has proven to be an extremely valuable tool. The images captured reveal the health and vigor of the plants through reflectance in key spectral bands, such as near-infrared, allowing for an accurate assessment of the crop’s condition. These findings not only confirm the effectiveness of multispectral technology but also support previous research highlighting the potential of drones for early and efficient agricultural monitoring and optimization.
Perhaps most encouraging was the response from the farming community. When given access to new tools and training, local producers showed both openness and enthusiasm for adopting modern approaches. Their willingness to experiment and learn was a reminder that innovation in agriculture is as much about people as it is about technology.
As the challenges facing tropical agriculture continue to evolve, the lessons from this study are broadly relevant. With continued collaboration, investment in farmer education, and a commitment to science-based decision-making, there is every reason to believe that cacao farming in the Dominican Republic-and in similar regions-can thrive for generations to come.
Supplementary Data
Supplementary figures, raw data, and R scripts used for statistical analyses are available upon request from the corresponding author.
Acknowledgements
The authors would like to express their sincere gratitude for the invaluable support provided by the FONDOCYT project, as well as for the collaborative efforts of local communities and farmers in the Dominican Republic who contributed to this research endeavor.
This manuscript was prepared in accordance with Oalib Journals guidelines and the recommendations from the “Get Published Quick Guide.” All co-authors have approved the final version and meet the criteria for authorship.
Conflicts of Interest
The authors declare no conflicts of interest.