bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.10.31.564942

Harvesting trajectories in large-scale coffee plantations: ecological and management drivers and implications

Abstract

CONTEXTCoffee is produced under different management systems and scales of production categorized as Syndromes of Production. The "Capitalist Syndrome" is characterized by the high use of capital and labor inputs to increase agricultural outputs. This syndrome results in practices like high planting densities that may promote the development and dispersal of plant pathogens like coffee leaf rust. The spatial arrangement of coffee trees drives the spatial movement of the harvesters, who can bear and disperse pathogens across and within plantations. In most capitalist coffee plantations, harvesters work multiple hours to maximize the daily harvest, which might increase their dispersal potential. However, their spatial movement has not yet been described, nor its relationship with the scale or management of the plantation, and even less its ecological implications for pathogens dispersal. OBJECTIVEWe describe and analyze the daily spatial movement of coffee harvesters in two large-scale capitalist plantations: an organic and a conventional plantation. METHODSUsing state-space models, we recorded and analyzed the spatial movements of harvesters. We then constructed a driver tree for harvest dynamics, which incorporated qualitative variables related to climate, coffee plants, and management aspects reported by the harvesters. RESULTS AND CONCLUSIONSOur model differentiated two kinds of movements: 1) when trees have berries, harvesters remain in the coffee rows or areas nearby (Collect state; 94-98% of the steps); 2) when not, harvesters make longer steps within the harvesting location or move to another area (Search state; 2-6% of the steps). In the organic plantation, the Search state had a longer-tailed step-length distribution than in the conventional plantation, resulting in a significantly higher visited area per worker (p<0.05). This might be related to a) a lower fruit load or percentage of trees with ripe fruits when we took the data or b) smaller harvesting locations ("pantes") per number of harvesters. Harvesting movements that explore a wider area, either by visiting more plants or by changing locations on the same day, could create more foci of CLR infection across the plantation. SIGNIFICANCEOur results highlight practices that can reduce the possible impact of human dispersal of pathogens like shorter harvesting trajectories by working fewer hours a day or avoiding harvesting at the end of the maturation season when few trees have berries and harvesters have to travel longer distances. This calls for organic coffee management that could prevent diseases, increase diversity, and guarantee just and safe conditions for workers.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mora Van Cauwelaert, E., Boyer, D., Jimenez-Soto, E., Benitez, M.. 2023-11-01. Harvesting trajectories in large-scale coffee plantations: ecological and management drivers and implications. https://doi.org/10.1101/2023.10.31.564942

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Operationalising the context in regenerative agriculture: decision-making and farm variability

Soil degradation is a widespread challenge that requires a broad response at the individual farm level. To ensure effectivity, the practices should be tailored to the farm context: land manager objectives and farm specific challenges. These have however been difficult to quantify. Here we demonstrate that a workable farm context can be created based on a value survey, open satellite and soil data, and published models for vegetation gross primary productivity and soil erosion. Based on the findings, despite individual differences, farmers value profitability and operational efficiency, but also biodiversity and soil health. At least the regenerative farmers surveyed also value working for the greater good more than maintaining tradition or power. In spite of wide differences in farm production orientation, we also found that each farm also had a broad variation in individual fields GPP. Most fields have a stable GPP level, which is either high or low, and that there is a 2-3-fold difference between the weakest and best producing fields indicating the potential for improving GPP by improving the growing conditions on currently weak fields. In addition, soil loss was found to be highly concentrated in critical source areas, where 10% of the field area contributed to 50% of the soil loss. Overall, open data can be linked to modelling workflows to rapidly produce a decision-making context for farmers. This facilitates benchmarking and co-learning as well as enables land managers and advisors to identify the farm context for planning effective responses to soil degradation.

ecology↗

Fly, land, listen: Autonomous intermittent locomotion enables scalable low-noise drone ecoacoustic surveys

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

ecology↗

Do higher-order moments improve inference of population dynamics?

Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a deterministic mathematical model to the average across replicates. We use a stochastic model to fit the variability to the observed variability across replicates. Using a simulation-driven approach, we study the conditions under which our approach allows us to infer a larger fraction of ecological parameters correctly. We observe a substantial improvement in parameter inference. Lastly, our Bayesian approach not only allows us to incorporate prior information about the system, but also provides a distribution of parameters which conveys some idea of the uncertainty of the estimates.

ecology↗