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Crop canopy optical reflectance and ultrasonic sensors provide a means of estimating the spatial variability of biomass and nitrogen uptake by sugarcane during the in-season period. The objective of this paper is to assess the crop canopy reflectance and ultrasonic crop height for predicting sugarcane biomass and N-uptake until the later fertilization stages. An ultrasonic sensor was deployed to measure canopy height, which were combined with optical reflectance sensor to characterize the spatial variability of the crop growth in four commercial fields in southeast Brazil during three different growing stages for dry and wet seasons. Ten sampling location points in each field were defined to determine plant biomass and N-uptake through traditional measurements. The points in each field were used to relate the actual biomass and N-uptake with the sensor data and compare them using the coefficient of determination and standard errors; this defined the best approach in each situation according to the multivariable statistics. It was found that both sensor systems enable to correlate its data with sugarcane biomass and N-uptake. Canopy reflectance sensor produced a better assessment of crop growth at the earlier growth stage whereas the ultrasonic sensor resulted in more accurate predictions at the later growing stages. It was proven that canopy height is season dependent whereas the reflectance data is growth stage dependent. The integration of both sensing systems improved the predictions of sugarcane biomass and N-uptake. It could be an alternative to guide local interventions by sugarcane industry during the growing season. [ABSTRACT FROM AUTHOR]

Coffee beverage quality is highly correlated with the degree of fruit ripeness. In this sense, monitoring fruit ripeness is of utmost importance for harvest planning and, especially for obtaining high-quality beverages. Currently, this process is carried out through manual counts of unripe fruits, which is laborious and limited to a few plants within the field. This study aimed at evaluating the potential of a low-cost multispectral camera for coffee ripeness monitoring in the Zona da Mata region of Minas Gerais State, Brazil. For that, five fields of Arabica coffee with distinct characteristics were evaluated. During the coffee ripeness period, four flights were carried using a Phantom 4 Pro quadcopter equipped with a Mapir Survey 3W camera for imagery acquisition. After that, nine vegetation indices (VIs) were obtained. For the same dates, the percentage of unripe fruits was obtained using an irregular grid in all fields. The data was split into two ripeness classes: suitable for harvest (R) with < 30% of unripe fruits; and not suitable for harvest (U), with > 30% of unripe fruits. Then, a principal component analysis was used to infer the importance of the VIs to discriminate plants with unripe fruits from those with ripe fruits. The first two principal components explained > 75% of the variance in the datasets from all coffee fields. The VIs were able to discriminate the ripeness classes (U and R) in most fields; however, their performance was directly influenced by the crop yield and canopy volume. [ABSTRACT FROM AUTHOR]

Nitrogen fertilization is challenging for sugarcane (Saccharum spp.) producers due to its complex interaction with the crop and soil. Thus, the main goal of this study was to develop a feasible approach to guide variable‐rate N application in sugarcane based on canopy sensor readings. This study was conducted for 5 yr. Several plot and strip N‐rate experiments were conducted under a wide range of crop conditions in Brazil and evaluated with the Crop Circle active canopy sensor (Holland Scientific Inc.). Because of variability in crop density and growth development within sugarcane fields, the use of an N‐rich reference area to estimate the crop response to N application was compromised. Biomass was the main crop parameter influencing canopy sensor readings, allowing yield estimation because biomass typically results in stalk yield. Thus, canopy sensor readings can efficiently predict relative sugarcane yield when working with data that are normalized to the mean for the field. Hence, an algorithm that takes into account this relationship was established. The concept of this algorithm is to apply higher N fertilization rates where the sugarcane yield potential is higher. Such an approach was determined to be useful to guide N application in sugarcane fields. Nevertheless, field validation is needed to confirm this N management strategy. Besides, more information about sugarcane biomass variability within fields may be required to increase algorithm efficiency. [ABSTRACT FROM AUTHOR]

This study had the objectives to estimate equations of biomass and carbon stock of Jatropha (Jatrophacurcas L.). The area of this study is located in Viçosa, Minas Gerais, planting carried out in 3.5x3.0m spacing at 4.64ha. To biomass quantification, we used the direct destructive method, applied to trees, which were selected according to the measurements of height, canopy diameter and number of branches. The tree biomass determination was obtained by the proportionality method. The adapted models were from Spurr and Schumacher & Hall, for biomass and total biomass (aboveground biomass more taproots biomass). The carbon stock was estimated multiplying the biomass by the carbon content of dry matter, which was obtained by calcination in a muffle. The estimate of the equivalent CO2 stored was obtained by multiplying the carbon stock by a factor 44/12. The equation with better model and who was used for carbon stock determination was to Spurr, using total biomass data, B=0.7601*(CD2*H)0.8949, where B = biomass (kg); CD = canopy diameter (m); e H = height (m). The carbon stock of culture, founded to the fourth year, was 6.79MgC ha-1, corresponding to 24.89MgCO2(eq) ha-1. The results shows that Jatropha is environmentally viable for development of CDM afforestation / reforestation projects or carbon projects to voluntary markets, adding income to farmers and improving the financial attractiveness of the crop. [ABSTRACT FROM AUTHOR]

• Outstanding yields by cane varieties were obtained under unlimited conditions in large field experiments in tropical Brazil. • Guadalupe, PI State, is a more favourable production environment for sugarcane than São Romão, MG State. • The radiation use efficiency was similar between varieties up to 6–8 months regardless of site. • The radiation use efficiency declined differently among varieties after the onset of the growth slowdown. • Traits such as stalk fraction and growth slowdown with ageing were derived for use in modelling with the APSIM-Sugar model. Aiming to gain an understanding of how the genotype × environment × management (G×E×M) interaction influences the yield accumulation by elite sugarcane varieties in Brazil, a large dataset from field plot experiments carried out in two tropical sites (Guadalupe, 6.8 °S; São Romão, 16.4 °S) involving distinct planting dates, varieties and harvest ages, was analysed with statistical techniques and with the APSIM-Sugar model. Radiation use efficiency (RUE) was determined via a series of regressions and employed in an analysis of variance to investigate site, seasonal, developmental, and varietal differences. Outstanding yields were achieved at both sites. RUE declined as the crop progressed, confirming previous observations on declining RUE with age, known as the reduced growth phenomenon (RGP). RUE was always greater at Guadalupe than São Romão, evidencing that Guadalupe is a more suitable environment for sugarcane production, favoured by higher air temperatures during crop establishment and canopy formation. Varietal differences in RUE appeared only after the early developmental stage, and the observed growth slowdown with age was consistent across the two experimental sites, indicating that RGP is a varietal trait that should be considered for high-yielding environments. The process-based APSIM-Sugar model was set up with recently determined canopy traits and a new RGP feature based on leaf appearance. RGP parameters were obtained for each variety and site through calibration. The calibrated model was accurate to account for yield accumulation by the varieties in both experiments. The new parameters were evaluated with independent datasets from other local experiments at each tropical site as well as from published rainfed experiments in sub-tropical Southeast Brazil. Independent verification of the RGP traits added confidence in the new way of dealing with RGP based on leaf stage. The G×E×M interaction on yield accumulation can now be explored more confidently with APSIM-Sugar for the purpose of optimising the choice of varieties, planting dates and harvest ages for sugarcane industries in favourable irrigated lands in tropical Brazil. [ABSTRACT FROM AUTHOR]

Coffee ripeness monitoring is a key indicator for defining the moment of starting the harvest, especially because the coffee quality is related to the fruit ripeness degree. The most used method to define the start of harvesting is by visual inspection, which is time-consuming, labor-intensive, and does not provide information on the entire area. There is a lack of new techniques or alternative methodologies to provide faster measurements that can support harvest planning. Based on that, this study aimed at developing a vegetation index (VI) for coffee ripeness monitoring using aerial imagery. For this, an experiment was set up in five arabica coffee fields in Minas Gerais State, Brazil. During the coffee ripeness stage, four flights were carried out to acquire spectral information on the crop canopy using two quadcopters, one equipped with a five-band multispectral camera and another with an RGB (Red, Green, Blue) camera. Prior to the flights, manual counts of the percentage of unripe fruits were carried out using irregular sampling grids on each day for validation purposes. After image acquisition, the coffee ripeness index (CRI) and other five VIs were obtained. The CRI was developed combining reflectance from the red band and from a ground-based red target placed on the study area. The effectiveness of the CRI was compared under different analyses with traditional VIs. The CRI showed a higher sensitivity to discriminate coffee plants ready for harvest from not-ready for harvest in all coffee fields. Furthermore, the highest R2 and lowest RMSE values for estimating the coffee ripeness were also presented by the CRI (R2: 0.70; 12.42%), whereas the other VIs showed R2 and RMSE values ranging from 0.22 to 0.67 and from 13.28 to 16.50, respectively. Finally, the study demonstrated that the time-consuming fieldwork can be replaced by the methodology based on VIs. [ABSTRACT FROM AUTHOR]