Coffee Yield Research Articles

Применение систем искусственного интеллекта в прогнозировании урожайности сельскохозяйственных культур

The use of artificial intelligence systems makes it possible to increase the accuracy of forecasting crop yields in comparison with traditional methods. The purpose of the study is to analyze research in the field of the use of artificial intelligence systems in predicting crop yields. Currently, artificial intelligence systems have been developed and tested to predict the yield of rice, wheat, lettuce, coffee and other agricultural crops. To do this, systems based on neural networks, genetic algorithms, the support vector machine and others are used. The effects of the use of artificial intelligence systems in predicting crop yields are revealed. They consist in improving the quality of the planning process of consumed resources, optimizing acreage, improving the accuracy of forecasting product prices in comparison with traditional methods. Specific recommendations are given on the use of artificial intelligence systems in optimizing grain production under the given constraints.

Forecasting arabica coffee yields by auto-regressive integrated moving average and machine learning approaches

<abstract> <p>Coffee is a major industrial crop that creates high economic value in Thailand and other countries worldwide. A lack of certainty in forecasting coffee production could lead to serious operation problems for business. Applying machine learning (ML) to coffee production is crucial since it can help in productivity prediction and increase prediction accuracy rate in response to customer demands. An ML technique of artificial neural network (ANN) model, and a statistical technique of autoregressive integrated moving average (ARIMA) model were adopted in this study to forecast arabica coffee yields. Six variable datasets were collected from 2004 to 2018, including cultivated areas, productivity zone, rainfalls, relative humidity and minimum and maximum temperatures, totaling 180 time-series data points. Their prediction performances were evaluated in terms of correlation coefficient (R<sup>2</sup>), and root means square error (RMSE). From this work, the ARIMA model was optimized using the fitting model of (p, d, q) amounted to 64 conditions through the Akaike information criteria arriving at (2, 1, 2). The ARIMA results showed that its R<sup>2</sup> and RMSE were 0.7041 and 0.1348, respectively. Moreover, the R<sup>2</sup> and RMSE of the ANN model were 0.9299 and 0.0642 by the Levenberg-Marquardt algorithm with TrainLM and LearnGDM training functions, two hidden layers and six processing elements. Both models were acceptable in forecasting the annual arabica coffee production, but the ANN model appeared to perform better.</p> </abstract>

Exploring the cooling effect of shading for climate change adaptation in coffee areas

Rising air temperatures are the main reason for the expected reduction in land suitability for coffee cultivation under climate change in Central America. One of the reasons farmers use shade trees is to create a cooler microclimate in coffee plantations located in warming areas; therefore, adjusting the shade levels could alleviate future high temperatures. Even though data on expected climatic changes are available, no studies have addressed the cooling potential of shading in coffee production systems. In this study, we use regional climate information (RCP 4.5) and a simple shade model to explore the potential of shading as an adaptation practice in the coffee areas in Central America. A model was developed to estimate the required shade levels for Coffea arabica L. based on mean air temperature. Modeled and observed shade data were compared. Results indicate that compared to 2000, an overall increment of 23 ± 18% of shading would be required to alleviate the warming conditions by 2050. The shading will be more beneficial to coffee areas at medium and high altitudes than to areas at low ones. Also, the number of coffee areas that require dense shade levels (shading > 60%) may double by 2050. This would lead to a boost in tree biomass (carbon content) but also increase the competition for the coffee plants and consequently affect coffee yields. Trade-offs between adaptation, mitigation, and productivity objectives are expected in the coffee areas in the future.

Populasi, Serangan, dan Sebaran Hama Hypothenemus hampei pada Kopi Arabika Gayo

<em>Hypothenemus hampei</em> (Ferrari) (Coleoptera: Scolytidae) or Coffee Berry Borer (CBB) is the most destructive major pest that causes quantitative and qualitative losses in Arabica coffee. The research aimed to calculate population, attack<em> H. hampei</em>, coffee yield losses, the measure distribution pattern of<em> H. hampei</em> on various Gayo arabica coffee varieties, and the analyze estimated decrease in the production of coffee based on population and attack<em> H. hampei</em> in Gayo arabica coffee. The location chosen as a place of observation and sampling is a smallholder coffee plantation with variations in elevation. Each area is determined by two points of observation and sampling locations. At each sampling location, 80 ripe or red coffees were taken, 80 dark or yellow coffees, and 80 young or green coffees (24 pieces per four branches per tree). The results showed that the population and attack of CBB pests on Gayo 3 varieties are relatively high compared to Gayo 1 and Gayo 2 varieties. The distribution pattern of CBB on all varieties of Gayo Arabica coffee showed an aggregated distribution based on population and attack. The decrease in arabica coffee production has the potential to occur on a variety Gayo 3.

THE CORRELATION OF ELEVATION, SOIL CHEMICAL PROPERTIES AND YIELD OF COFFEE ARABICA IN SHADED CONDITIONS

The research aims to analyze the correlation of altitude, soil chemical properties and shades on Arabica coffee yields in Humbang Hasundutan Regency Sumatera Utara Province. This research was conducted using survey method with purposive sampling approach. Sampling were conducted on altitude varied between 1200 – 1500 m asl. Soil samples in top soil and sub soil were taken in order to analyze pH, C, N, P2O5, K, and cation exchange capacity (CEC). Coffee plant growth variables observed including productive branches, number of bunches, number of fruit per bunches, diameter of canopy and total yield. Whereas the quality variable measured were physical quality and taste. Data on soil chemical properties, elevation, yield and coffee quality were analyzed using the correlation method. The results showed that under shaded condition, N, CEC, pH, K and P were positively correlated with height increase, while C-organic, productive branches, number of fruits per bunch and coffee production were negatively correlated. Whereas in the condition without shade, N, C, pH, K, productive branches, the number of fruit per bunch is negatively correlated with height increase, while CEC, P, number of bunches, diameter of canopy and coffee yield are positively correlated. Statistical data shows that under shaded condition, the strongest correlation showed by pH and P av (0.999), Whereas in an unshaded condition, yield and CEC showed the strongest correlation (0.994).

A Combination of Shade Trees and Soil Characteristics May Determine Robusta Coffee (Coffea canephora) Yield in a Tropical Monsoon Environment

Shade trees may have contrastive effects on Robusta coffee (Coffea canephora Pierre ex A. Froehner) yield, which may be additionally influenced by soil properties and reproductive stages of the plant. The current study is aimed at examining the interaction effects of shade trees, soils with different properties, and reproductive stages on coffee yield and identifying key factors influencing the yield. The current study was conducted in Dakha, Kontum of Vietnam on 133 farms, randomly selected from two soils, and two reproductive stages of coffee plants planted with and without shade trees. The coffee yield was measured, and surface soil samples were taken from these farms to be analyzed for 16 variables. The results showed that shade trees enhanced coffee yield significantly (by 28%) in the young reproductive stage but exhibited an insignificant effect in the old stage. The silt fraction, total nitrogen, cation exchange capacity, and shade trees were key factors determining 52% of the coffee yield in the sandy clay loam soil, while silt content and shade trees determined 23% of the yield in the clay soil. These findings suggest that shade trees, together with some soil properties, should be considered to improve coffee production under a tropical monsoon climate region.

A bitter future for coffee production? Physiological traits associated with yield revealhigh vulnerability to hydraulic failure in Coffea canephora.

The increase in frequency and intensity of drought events have hampered coffee production in the already threatened Amazon region, yet little is known about key aspects underlying the variability in yield potential across genotypes, nor to what extent higher productivity is linked to reduced drought tolerance. Here we explored how variations in morphoanatomical and physiological leaf traits can explain differences in yield and vulnerability to embolism in 11 Coffea canephora genotypes cultivated in the Western Amazon. The remarkable variation in coffee yield across genotypes was tightly related to differences in their carbon assimilation and water transport capacities, revealing a diffusive limitation to photosynthesis linked by hydraulic constraints. Although a clear trade-off between water transport efficiency and safety was not detected, all the studied genotypes operated in a narrow and/or negative hydraulic safety margin, suggesting a high vulnerability to leaf hydraulic failure (HF), especially on the most productive genotypes. Modelling exercises revealed that variations in HF across genotypes were mainly associated with differences in leaf water vapour leakage when stomata are closed, reflecting contrasting growth strategies. Overall, our results provide a new perspective on the challenges of sustaining coffee production in the Amazon region under a drier and warmer climate.

Seasonal Variation in Physiological Traits of Amazonian Coffea canephora Genotypes in Cultivation Systems with Contrasting Water Availability

Climate variation throughout the year affects photosynthesis and other physiological processes correlated with plant development and yield. This study aimed to evaluate the changes in the physiological attributes of Coffea canephora genotypes over the year in the Brazilian Amazon and assess their relationship with crop yield. The experiment was carried out in three cultivation systems with three genotypes. The evaluations were carried out in four periods: the peak of the dry season (S1); the beginning of the rainy season (S2); the peak of the rainy season (S3); and the beginning of the dry season (S4). A dataset of gas exchange, pigment indices, chlorophyll fluorescence, branch growth, and coffee yield was obtained. The group of gas exchange variables was the main contributor to treatment discrimination and was most affected by seasons. As expected, the values of gs, E, and A were significantly lower in S1, while the values of VPDLeaf-ar, TLeaf, and IWUE were significantly higher. Our results demonstrate that climatic seasonality affects the photosynthesis of Amazonian Robustas coffee, even under irrigated conditions, particularly in response to increased VPD. The physiological variables analyzed at the leaf level, even in different periods, did not explain the differences in the yield of C. canephora.

Estimating Coffee Plant Yield Based on Multispectral Images and Machine Learning Models

The coffee plant is one of the main crops grown in Brazil. However, strategies to estimate its yield are questionable given the characteristics of this crop; in this context, robust techniques, such as those based on machine learning, may be an alternative. Thus, the aim of the present study was to estimate the yield of a coffee crop using multispectral images and machine learning algorithms. Yield data from a same study area in 2017, 2018 and 2019, Sentinel 2 images, Random Forest (RF) algorithms, Support Vector Machine (SVM), Neural Network (NN) and Linear Regression (LR) were used. Statistical analysis was performed to assess the absolute Pearson correlation and coefficient of determination values. The Sentinel 2 satellite images proved to be favorable in estimating coffee yield. Despite the low spatial resolution in estimating agricultural variables below the canopy, the presence of specific bands such as the red edge, mid infrared and the derived vegetation indices, act as a countermeasure. The results show that the blue band and green normalized difference vegetation index (GNDVI) exhibit greater correlation with yield. The NN algorithm performed best and was capable of estimating yield with 23% RMSE, 20% MAPE and R² 0.82 using 85% of the training and 15% of the validation data of the algorithm. The NN algorithm was also more accurate (27% RMSE) in predicting yield.

Climate change scenarios, their impacts and implications on Indian cardamom-coffee hot spots; one of the two in the world

In this study, we investigated climatic parameters and predicted future changes in precipitation and atmospheric temperature levels based on RCP4. 5/8.5 scenarios in all cardamom-coffee hot spots of southern India. Our results showed more precipitation levels and patterns occurred in Cardamom hills (Kerala), followed by lower Puleny hills in Tamil Nadu. The least variation in precipitation levels has been noticed for temperate upper Puleny hills and Kodagu hills in Karnataka. RCP4.5/8.5 scenario analysis showed greater variability in precipitation, up to 180% increase and 90% decrease for all hot sports. The scenario analysis also predicted extreme temperature variations ranging from 0.5 to 8.5°C increase for the entire study region. A significant change in coffee yield and quality has been recorded over the last 30 years. Increased yield trends in coffee were noticed for Cardamom hills (CH) and Kodagu hills, but significantly lower coffee production was observed for lower Pulney hills. The mixed response of yield variability in coffee has been primarily attributed to the ongoing changing climatic factors. Ecophysiological studies of coffee, cardamom, and black pepper have proved that coffee would adapt well to future challenging climatic conditions, closely followed by cardamom and black pepper. Since all the coffee-cardamom hot spots in southern India undergoes considerable change in precipitation levels and pattern, necessary precautions, including water and irrigation management strategies, must be given utmost priority to increase the crop yield sustainability of these delicate cardamom-coffee hot spots in India.

The profile of soil fertility and yield of coffee in the land with a different slope (Case study in Way Tenong District, West Lampung Regency)

This study aims to analyze the level of soil fertility, the condition of the physical properties of the soil, and the level of soil erodibility on coffee plantations with various slope levels. Observations were made on coffee plantations in two villages in Way Tenong District, West Lampung Regency, namely Fajar Bulan Village and Puralaksana Village, in April 2022. The number of sampling points in each village is 6 points. Composite soil samples were taken to analyze the chemical properties of the soil, including pH, C-Organic, N-Total, P content, and soil cation exchange capacity. Meanwhile, intact soil samples were used to determine the values of bulk density, soil porosity, soil texture, and soil moisture content. The analysis was carried out at the Laboratory of Institute for Agricultural Technology Assessment (BPTP) Lampung. Based on the soil pH levels at the sampling location, the soil reactions were acid with pH < 5, with only one location with a pH above 5 (FB 1). Soil organic matter content in most locations was still low, except for FB 1 location. The slope of the slopes at the soil sampling location varied from flat (0 - 10%) to mountainous (>60%). Based on physical properties, soil density is generally high (the highest BD is 1.59) and low porosity value. The coffee yields tend to decline from 2019-2021.

Genetic Gain Trend in Yield and Advanced Selection of Yayo Coffee (Coffea arabica L.) Land Race Collection

Coffea arabica L., the dominant cash crop in the world market, is native to rain forest of Ethiopia where it is believed to exist with high genetic diversity. Estimating genetic parameters are momentous in deciding breeding method to be followed for crop genetic improvement including Arabica coffee. The study was conducted with the intention to gauge genetic gain trend in coffee yield and to select advanced promising lines of Yayo coffee landrace for the next breeding step. The study was laid down at Metu research subcenter in 2013, using 124 coffee accessions that were established in simple lattice design under two sets each comprising 62 accessions including two checks. The over six year’s pooled analysis of variance indicated the handiness variability in yield performance among accessions. Moderate genotypic variance (15.46 to13.56%), heritability (56.16–81%), and expected genetic gain (15.52–20.8%) were observed. The genetic parameters and the superiority of check in yield over accessions elucidated that high yielder variety development by selection is difficult unless heterosis attaining breeding method followed, particularly for these Yayo coffee landrace origin. Common high genetic gain trend (49.19 and 100 kg·ha−1) and response to selection (196.76 and 400 kg·ha−1), selection differential 471.9 and 739.23 kg·ha−1 were revealed in over four harvesting seasons mean value for both sets. Thus, selection is more effective in earlier season than late. High yielding accessions, Y27 and Y93, gave 3013.1 and 125.8 kg·ha−1 yield gain over the high yielder check correspondingly. Despite the top 15 and 10 high yielders were selected from set-I and set-II, respectively, a total of 20 accessions with contrasting desirable traits were selected and established in crossing block for genetic improvement purposes via heterotic hybrid variety development program. These accessions were tolerant to major coffee disease and have desirable agronomic traits.

Pertumbuhan dan Hasil Kopi Grafting Robusta/Liberika pada Klon dan Waktu Pemupukan yang berbeda

Grafting of different coffee species (interspecific grafting) aims to take advantage of the superiority of the rootstock and promote the growth and production of the scion. For example, Robusta coffee is grafted with Liberika coffee as rootstock. One of the principles of fertilization is the application of fertilizer at the right time. This study aimed to determine the effect of clones of Robusta/Liberica interspecific grafting and different fertilization times on the growth and yield of Robusta coffee. The research was conducted in a 15-year-old coffee plantation with the rootstock of Liberika coffee of the Red Robinson variety which was grafted to the scion of multiclonal Robusta coffee. Planting distance of coffee was 2.5 m x 2.5 m and shade trees were Gliricidia sepium. This research used a split plot design arranged in a factorial (4x2) with three replications so that there were 24 experimental units. The main plot was the time of fertilization, namely in early April and October and in early May and November. As sub-plots, there were Robusta coffee clones, namely Korolla 1, Korolla 2, Tugu Bungkuk, and Darmani. The results showed that fertilization time, clones, and their interactions did not affect the number of primary branches, primary branch length, number of secondary branches, fruit weight of harvested samples, number of fruit per bunch, fruit weight per bunch, dry seed weight per bunch, and coffee yield per tree. The interaction of coffee clones and fertilization time had a significant effect on the number of pods per branch, the number of harvested samples, dry bean weight of harvested samples, dry bean weight per branch, and coffee yield per hectare. For Darmani coffee clones, delaying fertilization from April and October to May and November reduced coffee yield. Key words : Interspesific grafting, Liberica, Robusta, time of fertilization, yield

Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning

Coffee has high relevance in the Brazilian agricultural scenario, as Brazil is the largest producer and exporter of coffee in the world. Strategies to advance the production of coffee grains involve better understanding its spatial variability along fields. The objectives of this study were to adjust yield-prediction models based on a time series of satellite images and high-density yield data, and to indicate the best phenological stage of coffee crop to obtain satellite images for this purpose. The study was conducted during three seasons (2019, 2020 and 2021) in a commercial area (10.24 ha), located in the state of Minas Gerais, Brazil. Data were obtained using a harvester equipped with a yield monitor that measures the volume of coffee harvested with 3.0 m of spatial resolution. Satellite images from the PlanetScope (PS) platform were used. Random forest (RF) regression and multiple linear regression (MLR) models were fitted to different datasets composed of coffee yield and time series of satellite-image data ((1) Spectral bands—red, green, blue and near-infrared; (2) Normalized difference vegetation index (NDVI); or (3) Green normalized difference vegetation index (GNDVI)). Whether using RF or MLR, the spectral bands, NDVI and GNDVI reproduced the spatial variability of yield maps one year before harvest. This information can be of critical importance for management decisions across the season. For yield quantification, the RF model using spectral bands showed the best results, reaching R2 of 0.93 for the validation set, and the lowest errors of prediction. The most appropriate phenological stage for satellite-image data acquisition was the dormancy phase, observed during the dry season months of July and August. These findings can help to monitor the spatial and temporal variability of the fields and guide management practices based on the premises of precision agriculture.

Genotype by Environment Interaction and Yield Stability of Coffee (Coffea arabica L.) Genotypes Evaluated in Western Ethiopia

ABSTRACT The performance of genotypes under diverse environments can be used to determine their adaptability and stability. However, information on the performance of coffee genotypes in various environmental conditions is limited. Thus, this study’s objectives were to estimate genotype by environment interaction (GEI), evaluate the mean performance and stability of 16 fruity flavored coffee genotypes in eight specialty coffee growing environments in western Ethiopia, and assess the magnitude of correlations among different stability parameters. The experiment was laid out in a randomized complete block design (RCBD) with two replications. For coffee yield, data were recorded and a combined analysis of variance and stability analysis were performed. Additive main effect and multiplicative interaction (AMMI) analysis revealed that genotypes, environments, and GEI showed highly significant differences (P < .0.01) for coffee bean yield. AMMI analysis also revealed that 73.2% of the GEI sum of squares for coffee bean yield was accounted for by the first three interaction principal component axes (IPCA). The standard check variety G16 (Menesibu), G3(W54/99), and G10 (W107/99) gave the highest average yields of 1537, 1458 and 1375 kg of clean coffee per hectare across environments, respectively. Despite no genotypes consistently performing well across environments due to the high GEI, G1 (W13/99) and G5 (W54/99) were relatively stable. Therefore, these were recommended as useful genetic resources for breeding of high-yielding genotypes. However, since all the genotypes gave a mean yield below the standard check variety, additional genotypes should be tested in more environments to develop stable and high-yielding coffee varieties.

Coffee Yield Stability as a Factor of Food Security.

Yield fluctuation is a major risk in all agricultural sectors, and it influences Goal 2 (food security) of the UN SDGs. Yield fluctuations are expected due to climate change, risking stable coffee supplies, and compromising coffee-exporting countries’ ability to earn revenue to pay for food imports. Technology minimizing yield fluctuations is crucial for food security and for coffee farmers to earn a stable income. Fluctuations are small if yields remain close to the mean yield trends. In this study, the coffee yields of major producers are analyzed, together with zonal temperature data, to see where coffee is grown with stable technology under rising temperatures; thus, we demonstrate the advantages of the Yield Stability Index (YSI) over traditional stability measurements in guiding policy formulation and managerial decisions. The Yield Stability Index (YSI) is applied for 1961–1994 and 1995–2020, for the world’s 12 major coffee-producing countries. The YSI indicates that of the 12 countries, only Indonesia, Honduras, and Mexico maintain stable yield levels, while Brazil and Vietnam considerably improve their yield stability, which traditional stability measures cannot grasp. Country-wise differences exist in environmental vulnerability and adaptability, with implications for food security. The novelty is the application of the YSI, and the connection between yield stability, climate change, and food security.

Assessing the Temporal and Spatial Variability of Coffee Plantation Using RPA-Based RGB Imaging

The biophysical parameters of coffee plants can provide important information to guide crop management. An alternative to traditional methods of sparse hand measurements to obtain this type of information can be the 3D modeling of the coffee canopy using aerial images from RGB cameras attached to remotely piloted aircraft (RPA). This study aimed to explore the use of RGB aerial images to obtain 3D information of coffee crops, deriving plant height and volume information together with yield data during three growing seasons in a commercial production area of 10.24 ha, Minas Gerais state, Brazil. Seven data acquisition campaigns were conducted during the years 2019, 2020 and 2021. The flights were made at 70 m above ground level, with lateral and longitudinal overlaps of 75% and 80%, respectively. The images were processed, obtaining canopy surface models (CSMs) derived into plant height and volume data for each campaign. The results showed that it is possible to extract the plant height of coffee plants with an R2 of 0.86 and an RMSE of 0.4 m. It was possible to monitor the temporal variability of coffee plant height and volume based on aerial images and correlate this information with yield data. The results of the modeling analysis demonstrated the possibility of using these variables to help understand the spatial variability of coffee yield within the field.

Early flowering changes robusta coffee yield responses to climate stress and management

A shift towards earlier flowering is a widely noted consequence of climate change for the world's plants. However, whether early flowering changes the way in which plants respond to climate stress, and in turn plant yield, remains largely unexplored. Using 10 years of flowering time and yield observations (Total N = 5580) from 558 robusta coffee (Coffea canephora) farms across Vietnam we used structural equation modelling (SEM) to examine the drivers of flowering day anomalies and the consequent effects of this on coffee climate stress sensitivity and management responses (i.e. irrigation and fertilization). SEM allowed us to model the cascading and interacting effects of differences in flowering time, growing season length and climate stress. Warm nights were the main driver of early flowering (i.e. flowering day anomalies <0), which in turn corresponded to longer growing seasons. Early flowering was linked to greater sensitivity of yield to temperature during flowering (i.e. early in the season). In contrast, when late flowering occurred yield was most sensitive to temperature and rainfall later in the growing season, after flowering and fruit development. The positive effects of tree age and fertilizer on yield, apparent under late flowering conditions, were absent when flowering occurred early. Late flowering models predicted yields under early flowering conditions poorly (a 50 % reduction in cross-validated R2 of 0.54 to 0.27). Likewise, models based on early flowering were unable to predict yields well under late flowering conditions (a 75 % reduction in cross-validated R2, from 0.58 to 0.14). Our results show that early flowering changes the sensitivity of coffee production to climate stress and management and in turn our ability to predict yield. Our results indicate that changes in plant phenology need to be taken into account in order to more accurately assess climate risk and management impacts on plant performance and crop yield.

Cultivation, Yield, Income, and Tree Diversity in a Coffee Field in the Eastern Ghats of Andhra Pradesh, India

In this study we analyse coffee cultivation, yield, revenue and tree diversity in coffee forest regimes in Eastern Ghats of Andhra Pradesh. The coffee growing regions in India are mainly confined to the traditional South Indian states that are Karnataka, Kerala and Tamil Nadu and partly in non-traditional regions are Andhra Pradesh and Odisha. While to a smaller extent in North-Eastern states. The production of coffee has an impact on the national and international economies and is the second-most traded commodity in the world. Total area under coffee plantations in Andhra Pradesh was about 4010 ha. The Average yield of Andhra Pradesh coffee is roughly 363.9026MT per year and the average revenue is 629.158(in lakhs) per year. Deforestation and the loss of biodiversity are two effects of the expansion of coffee farming. However, shade-grown coffee has been hailed as a way to safeguard tropical biodiversity. If promoted, shade-grown coffee can help preserve some biodiversity, but it can't match a natural forest's whole range of ecological advantages. The coffee cultivated in Andhra Pradesh is mainly shade grown coffee. In this study we also focused on tree species diversity, we analysed data on tree species diversity from 20 field plots (0.1 ha) in coffee area of, Eastern Ghats, Andhra Pradesh, India. We also analyse diversity indices evenness indices species richness indices and Density, frequency, basal area and IVI also calculated. Forest ecosystems can be protected against rising levels of disruption by increasing the diversity of tree species. The variety of tree species may also be directly impacted by anthropogenic disturbance.

Use of Active Sensors in Coffee Cultivation for Monitoring Crop Yield

Monitoring the spatial variability of agricultural variables is a main step in implementing precision agriculture practices. Active optical sensors (AOS), with their instrumentation directly on agricultural machines, are suitable and make it possible to obtain high-frequency data. This study aimed to evaluate the potential of AOS to map the spatial and temporal variability of coffee crop yields, as well as to establish guidelines for the acquisition of AOS data for sensing the sides of a coffee plant, allowing the evaluation of large commercial fields. The study was conducted in a commercial coffee area of 10.24 ha, cultivated with the Catuaí 144 variety. Data collection was performed with six Crop Circle ACS 430 sensors (Holland Scientific, Lincoln, NE, USA) and two N-Sensor NG sensors (Yara International, Dülmen, Germany). Seven field expeditions were made to collect data using the optical sensors during 2019 and 2021, obtaining data during the flowering, fruit-filling and fruit maturation phases (pre-harvest), and post-harvest. The results showed that the different faces of the same plant present a different Pearson’s correlation coefficient (r) to its yield, obtained with a yield monitor on the harvester. The face with the highest exposure to solar radiation presented a slightly higher correlation to yield (−0.34 ≤ r ≤ −0.17) when compared with the face with less exposure (−0.27 ≤ r ≤ −0.15). In addition, it was observed that the vegetation indices measured at the beginning of the coffee cycle (before the rainy season that starts in October) present a positive correlation to the coffee yield of that same year (0.73 ≤ r ≤ 0.91). On the other hand, this relationship is changed after the beginning of the rain season, at which time the vegetation index increases abruptly, inverting the correlation with the yield after that (−0.93 ≤ r ≤ −0.77). Furthermore, it was observed that, due to the biennial nature of coffee production, the vegetation index acquired at a specific time has an inverted relationship when compared with the yield of that year and to the yield of the following (or previous) year.