## 1 Introduction  
Changes in the Caatinga biome have already resulted in it reaching approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, behind only the Atlantic forest and Cerrado [1, 2]. Although cattle raising is one of the main activities in the region, it is among the main sources of interference in the Caatinga due to the extensive practice of grazing, non-adjustment of grazing pressure, and grazing at inappropriate times, which are driven by the difficulty of monitoring and estimating yield parameters of forage plants [3, 4, 5].  
Agroforestry systems (AFSs) are quite relevant in reconciling economic and environmental biases, contributing to several points, such as integrated land use, recovery of areas, conservation and promotion of biodiversity, reduction in erosion, and emission of greenhouse gases [6; 7]. Denoting the importance of studies focused on the use of AFS in the Caatinga biome and the development of models that help in the process of monitoring and decision making of the producer, especially considering all the complexity and management needs that AFSs involve, it is essential to improve remote sensing techniques, which is a relevant tool in the process of monitoring and decision making in the agricultural environment [8; 9]. Since it allows for information to be obtained remotely and reduces the need for slow, destructive field analysis, it can be used for, among its many applications, the estimation of crop yields.  
Using unmanned aerial vehicles (UAVs) in monitoring productive areas has shown high growth over the years, mainly due to advances in altitude, camera quality, ease of flight, and image processing [10]. According to the literature consulted, several studies use this technology, relying mainly on the use of vegetation indexes [11; 12; 13; 14], thus enabling the identification of yield traits of vegetation. These indexes relate different electromagnetic spectrum bands, starting from the interactions that the bands have with plant contact surface characteristics, which are influenced mainly by water content, chlorophyll content, and carotenoid species [15].  
Several studies have applied vegetation indexes in conventional agricultural and agroforestry systems [12; 16; 17; 18]. However, there is still a scarce number of studies that consider, in the modeling process, ways to circumvent the inherent complexity of the compositions of the systems, mainly those inserted in the Caatinga biome. Agforestry systems in the Caatinga biome present a unique challenge due to their diverse combinations of species with varying morphological and physiological structures. These variations significantly influence the relationships between vegetation indices and vegetative characteristics, particularly within AFS in the Caatinga biome.  
Different modeling techniques are constantly used in the literature [19; 20; 21; 22; 23; 24], among these, machine learning methods stand out as an important tool in the process of generating predictive models, relying on algorithms that are capable of learning during model definition and therefore making the most of information as they quickly characterize complex patterns in the input data. Thus, it is possible to improve the models obtained in the process of automating tools for decision support [25; 26; 27; 28]. Therefore, it is essential to compare this method with other models, especially classical and widespread models [29].  
Given the above, it was hypothesized that the vegetation indexes are viable alternatives for estimating the yield parameters of forage crops in agroforestry systems in the Caatinga biome. Consequently, the present study aimed to compare the predictive ability of different vegetation indexes on the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in agroforestry systems in the Caatinga biome and evaluate the influence of removing system components on model performance.