Predicting Sugarcane Yield Research Articles

Climate, altitude, yield, and varieties drive lodging in sugarcane: A random forest approach to predict risk levels on a tropical island

Lodging is a critical factor in reducing sugarcane yields worldwide, mainly due to the selection of highly productive varieties. Understanding the response of yield and lodging to the combined effects of climate, sugarcane traits, and varieties has become a priority under climate change. The aim of this study was to better understand the influence of plant characteristics, climate, and soil conditions on the trade-off between sugarcane yield and lodging on the tropical Reunion Island. Data from a 14-year experimental network run by the eRcane breeding institute were used to build random-forest models to predict sugarcane yield and lodging classes, i.e. <10 %, 10–50 %, >50 % of lodging. Yield and lodging probability were then predicted across the island using climate change projections from 2015 to 2035. Both yield and lodging were highly influenced by the variety and characteristics (height and tillering) and climatic conditions. Areas on the island at high altitudes were subject to high probability of lodging (>50 %), while in areas with high wind speed, the risk of moderate lodging (10–50 %) increased. Overall, conditions or plant characteristics that favor higher yields increased lodging probability. Nevertheless, the correlation between yield and lodging probability varied considerably depending on the variety, highlighting the importance of sugarcane characteristics in resistance to lodging. This study highlights the fact that promoting more productive varieties in recent decades has led to an increase in lodging and identified critical environments on the island prone to increased risk of lodging.

Regional Model to Predict Sugarcane Yield Using Sentinel-2 Imagery in São Paulo State, Brazil

Regional Model to Predict Sugarcane Yield Using Sentinel-2 Imagery in São Paulo State, Brazil

Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model

As a raw material for sugar, ethanol, and energy, sugarcane plays an important role in China’s strategic material reserves, economic development, and energy production. To guarantee the sustainable growth of the sugarcane industry and boost sustainable energy reserves, it is imperative to forecast the yield in the primary sugarcane production regions. However, due to environmental differences caused by regional differences and changeable climate, the accuracy of traditional models is generally low. In this study, we counted the environmental information and yield of the main sugarcane-producing areas in the past 15 years, adopted the LSSVM algorithm to construct the environmental information and sugarcane yield model, and combined it with WCA to optimize the parameters of LSSVM. To verify the validity of the proposed model, WCA-LSSVM is applied to two instances based on temporal differences and geographical differences and compared with other models. The results show that the accuracy of the WCA-LSSVM model is much better than that of other yield prediction models. The RMSE of the two instances are 5.385 ton/ha and 5.032 ton/ha, respectively, accounting for 7.65% and 6.92% of the average yield. And the other evaluation indicators MAE, R2, MAPE, and SMAPE are also ahead of the other models to varying degrees. We also conducted a sensitivity analysis of environmental variables at different growth stages of sugarcane and found that in addition to the main influencing factors (temperature and precipitation), soil humidity at different depths had a significant impact on crop yield. In conclusion, this study presents a highly precise model for predicting sugarcane yield, a useful tool for planning sugarcane production, enhancing yield, and advancing the field of agricultural production prediction.

Dry Rot Caused by the Complex Colletotrichum falcatum and Thielaviopsis paradoxa Emerges as a Key Stalk Disorder in Newly Expanded Sugarcane Plantations from Northwestern São Paulo, Brazil

Sugarcane dry rot emerged as an important stalk disorder in newly expanded plantations in northwestern São Paulo, Brazil, under the current no-burning fully mechanical harvest policy gradually implemented in the past 20 years. This emergence was probably due to a considerable increase in both pathogen inocula and insect pest populations in sugarcane crop residues kept in the field. In this study, we surveyed the incidence of three stalk-related disorders in commercial sugarcane fields in six municipalities in northwestern São Paulo and the corresponding yield losses. The three stalk-related disorders surveyed were as follows: the red rot disease caused by the fungal pathogen Colletotricum falcatum, the spittlebug-induced shoot stunting, and the stem dry rot, which is associated with the simultaneous infection of C. falcatum and Thielaviopsis paradoxa, the pineapple set rot pathogen. Red rot disease was detected in 88.2% of the fields surveyed, while the spittlebug-induced shoot stunting disorder and the internal stem dry rot were found in 97.1% of the fields. Stem dry rot had the highest incidence and resulted in the highest yield losses. Total sugarcane yield losses were estimated at 20.1%, with an average of 14.2 (±3.8) t·ha−1 per field. The multiple regression model constructed to determine which of the three stem-related disorders contributed the most to total yield losses was not significant. Subsequently, the performance analyses of single-variable polynomial regression models indicated that the simple linear model was the best fit in terms of independently predicting sugarcane yield losses based on each stem-related disorder. Positive and significant correlations were only detected between sugarcane yield losses in t·ha−1 and the incidence of red rot disease or leafhopper-induced shoot stunting. We concluded that the stalk’s internal dry rot, as a disease complex associated with both C. falcatum and T. paradoxa, was the most important disorder in sugarcane fields in the northwest region of São Paulo state. A sustainable pest management program is needed to reduce the impact of all three stalk-associated disorders on regional sugarcane production.

Machine learning model ensemble for predicting sugarcane yield through synergy of optical and SAR remote sensing

Pre-harvest estimate of sugarcane production is required by sugar mill officials for proper planning about intra or inter-regional trading of sugarcane if expected production is more or less than mill's crushable capacity. Integration of optical and synthetic aperture radar (SAR) remote sensing has shown to improve biomass prediction accuracy of a perennial crop like sugarcane, particularly when optical data is unavailable due to presence of clouds. This study aims at estimating sugarcane yield using optical data from Sentinel-2 and SAR data from Sentinel-1 at mill catchment level of four sugar mills in Gujarat and Maharashtra, India. A variety of machine learning (ML) algorithms, including those based on Bayesian inference, as well as ensemble methods like bagging or boosting, were utilized to predict biomass. Additionally, a specific type of ensemble technique known as model stacking was also employed for predicting cane biomass. Fusing optical and SAR based yield driving variables from different active growth phases of sugarcane in an ensemble modeling framework explained about 63–70% variations of pixel-level above ground biomass during model training and 44–60% during testing stage. Aggregation of pixel level sugarcane yield at micro-zone level (cluster of villages) showed good prediction accuracies in Gujarat (NRMSE of 18%) and Maharashtra (NRMSE of 32%) at least 1–2 months before harvesting.

UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane.

Predicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.

Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning

Multispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors. The research was conducted on five varieties of sugarcane, as a way to make a robust approach. Multispectral images were collected every 40 days and the evaluated biometric parameters were: number of tillers (NT), plant height (PH), and stalk diameter (SD). Two ML models were used: multiple linear regression (MLR) and random forest (RF). The results showed that models for predicting sugarcane NT, PH, and SD using time series and ML algorithms had accurate and precise predictions. Blue, Green, and NIR spectral bands provided the best performance in predicting sugarcane biometric attributes. These findings expand the possibilities for using multispectral UAV imagery in predicting sugarcane yield, particularly by including biophysical parameters.

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

Predicting the sugarcane yield in real-time by harvester engine parameters and machine learning approaches

Information regarding sugarcane yield is the starting point for the application of Precision Agriculture (PA) strategies to the management of sugarcane fields. Over the years, many sugarcane yield monitoring approaches have been developed and tested, but its use is still limited. One option is to use data generated by the sugarcane harvester provided in the controller area network (CAN), which allows getting the messages related to the harvester operation. Our goal was to train and to test random forest (RF), multiple linear regression (MLR), and artificial neural network (ANN) using CAN data available on the on-board computer of the harvester. All predictive models were trained and tested using the main engine parameters: fuel consumption, engine rotation, engine power, and specific fuel consumption (SFC) as data input. A scale yield monitor was installed on the harvester elevator to provide the georeferenced yield data. The yield prediction accuracy of the predictive models using the four parameters was compared. All models followed the observed sugarcane yield and recognized the changes over the time of data collection. The MLR and ANN models have a higher error of prediction in the extreme values of yield. Over the testing time, the ANN underestimated the sugarcane yield, and the RF prediction by the engine parameters of the harvester was coming closest to the observed yield. The best results for predicting sugarcane yield by engine parameters were obtained from the RF model with a mean absolute percent error (MAPE) of 5.6%, and root mean square error (RMSE) of 7.0 Mg ha−1. The MLR and ANN models have a MAPE of 7.8% and 5.6%, respectively. All the variables were necessary for a better performance of the models; however, SFC was the most important variable for predicting sugarcane yield.

Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning

Abstract An accurate model for predicting sugarcane yield will benefit many aspects of managing growth and harvest of sugarcane crops. In this study, Sentinel-1 and Sentinel-2 satellite imagery were used in combination with climate, soil and elevation data to predict field-level sugarcane yield across the multiple sugar mill areas in the Wet Tropics of Australia at different time steps over four consecutive growing seasons (2016–2019). A total of ≈1400 field-level measurements were used to train predictive machine learning models of cane yield (t/ha), commercial cane sugar (CCS, %), sugar yield (t/ha), crop varieties and ratoon numbers. We compared the predictive performance of models based on both satellite imagery only and a fusion of satellite imagery with climate, soil and topographical information. Randomized search on hyperparameters was the method used to optimize and identify the most accurate decision tree-based machine model. Overall, gradient boosting was the most accurate method for predicting sugarcane attributes. The analysis resulted in cane yield, CCS and sugar yield predicted at the field level with R2 of up to 0.51 (RMSE = 16 t/ha), 0.63 (RMSE = 1 %) and 0.62 (RMSE = 2 t/ha) as soon as four months before the harvest season. It was also found that sugarcane varieties could be mapped with an accuracy of up to 73.4 %, while the differentiation of planted and ratoon crops exhibited the lowest accuracy of 45.4 %. Using a novel SHapley Additive exPlanations (SHAP) approach to explain the output of our machine learning models we found that Sentinel-2 derived spectral indices were the most important in predicting cane yield as well as differentiating sugarcane varieties and ratoon numbers. In contrast, climate and elevation derived predictors were the most important in predicting CCS and sugar yield. At the whole sugar mill area level, spatially averaged field-level results predicted mill area cane yield, CCS and sugar yield with R2 of 0.75 (RMSE = 4.6 t/ha), 0.80 (RMSE = 0.6 %) and 0.77 (RMSE = 1 t/ha). Early season prediction of sugarcane yields at both field- and mill-area level could be valuable for informing fertilizer application, harvest scheduling and marketing decisions.

Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level

Early prediction of sugarcane crop yield at the commercial block level (unit area of a single crop of the same variety, ratoon or planting date) offers significant benefit to growers, consultants, millers, policy makers, crop insurance companies and researchers. This current study explored a remote sensing based approach for predicting sugarcane yield at the block level by further developing a regionally specific Landsat time series model and including individual crop sowing (or previous seasons’ harvest) date. For the Bundaberg growing region of Australia this extends over a five months period, from July to November. For this analysis, the sugarcane blocks were clustered into 10 groups based on their specific planting or ratoon commencement date within the specified five months period. These clustered or groups of blocks were named ‘bins’. Cloud free (&lt;20%) satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors were acquired over the cane growing region in Bundaberg (area of 32,983 ha), from the growing season starting in July 2014, with the average green normalised difference vegetation index (GNDVI) derived for each block. The number of images acquired for each season was defined by the number of cloud free acquisitions. Using the Simple Linear Machine Learning (ML) algorithm, the extracted Landsat derived GNDVI values for each of the blocks were converted to Sentinel GNDVI. The average GNDVI of each ‘bin’ was plotted and a quadratic model was fitted through the time series to identify the peak growth stage defined as the maximum GNDVI value. The model derived maximum GNDVI values for each of the bins were then regressed against the average actual yield (t·ha-1) achieved for the respective bin over the five growing years, producing strong correlations (R2 = 0.92 to 0.99). The quadratic curves developed for the different bins were shifted according to the specific planting or ratoon date of an individual block allowing for the peak GNDVI value of the block to be calculated, regressed against the actual block yield (t·ha-1) and the prediction of yield to be made. To validate the accuracies of the 10 time series algorithms representing each of the 10 bins, 592 individual blocks were selected from the Bundaberg region during the 2019 harvest season. The crops were clustered into the appropriate bins with the respective algorithm applied. From a Sentinel image acquired on the 5 May 2019, the prediction accuracies were encouraging (R2 = 0.87 and RMSE = 11.33 (t·ha-1)) when compared to actual harvested yield, as reported by the mill. The results presented in this paper demonstrate significant progress in the accurate prediction of sugarcane yield at the individual sugarcane block level using a remote sensing, time-series based approach.

Sugarcane Yield Prediction Through Data Mining and Crop Simulation Models

The understanding of the hierarchical importance of the factors which influence sugarcane yield can subsidize its modeling, thus contributing to the optimization of agricultural planning and crop yield estimates. The objectives of this study were to identify and ordinate the main variables that condition sugarcane yield, according to their relative importance, as well as to develop mathematical models for predicting sugarcane yield by using data mining (DM) techniques. For this, three DM techniques were applied in the analyses of databases of several sugar mills in the state of Sao Paulo, Brazil. Meteorological and crop management variables were analyzed through the following DM techniques: random forest; boosting; and support vector machine, and the resulting models were tested through the comparison with an independent data set. Finally, the predictive performances of these models were compared with the performance of a simple agrometeorological model, applied in the same data set. The results allowed to conclude that, within all the variables assessed, the number of cuts was the most important factor considered by all DM techniques. The comparison between the observed yields and those estimated by the DM models resulted in a root mean square error (RMSE) ranging between 19.70 and 20.03 t ha−1, which was much better than the performance of the Agroecological Zone Model, which presented RMSE ≈ 34 t ha−1.

Supervised Machine Learning Techniques for Predicting Sugarcane Yield

Agriculture is the most important sector in the Indian economy and contributes 18% of Gross Domestic Product(GDP). India is the second largest producer of sugarcane crop and produces about 20% of the world's sugarcane. Sugarcane is cultivated in tropics and subtropic regions, on a wide range of soils from fertile well-drained mollisols to through heavy cracking vertisols, infertile acid oxisols, peaty histosols, to rocky andisols. Minimum moisture of 60cms, rich water supply and plenty of sunshine. In this paper, a novel approach to sugarcane yield forecasting in Karnataka, India region using Long Term Time Series(LTTS), weather-and-soil attributes, Normalized Vegetation Index(NDVI) and Supervised Machine Learning(SML) algorithms have been proposed. Sugarcane cultivation life cycle(SCLC) in the Karnataka region is about 12 months, with plantation beginning at three different seasons in weather condition. Our approach has been verified using historical dataset and results have shown that our approach has successfully modeled crop prediction. The application of the Custom-Kernel gives us a considerable boost in accuracy with SVM-Kernel Multiple giving 86.31% of accuracy, SVM-RBF kernel in second with an accuracy of 83.40%, GPR producing an accuracy score of 81.75%, Lasso giving an accuracy score of 26.81% and Kernel Ridge-RBF with the least accuracy score of 21.46% for final yield prediction.

Prediction of Sugarcane Yield by Soil Attributes under Straw Removal Management

Core Ideas Soil changes induced by short‐term sugarcane straw removal did not influence the crop yield. The yield of sugarcane straw and stalk can be predicted using soil attributes under fields managed with straw removal. Meteorological conditions may alter the straw/stalk ratio and thus influence the sugarcane straw yield. Sugarcane (Saccharum spp.) straw removal from the field has the potential to produce short‐term gains at the cost of long‐term sustainability. The objective of this study was (i) to develop a model capable to predict sugarcane yield (straw and stalk) by soil attributes and (ii) to discover why yields are minimally impacted following straw removal. In this 2‐yr experiment, the sugarcane straw removal effects on crop yields and soil attributes were investigated at two sites, Valparaíso and Capivari, in southeastern Brazil. Soil samples from the 0–5, 0–10, 0–20, and 0–30 cm were analyzed for C, N, Ca, Mg, P, K, pH, bulk density (BD), and soil penetration resistance (PR). The data were subjected to descriptive statistical, geostatistical, correlation and regression analyses. The findings showed that the straw and stalk yield can be predicted using soil attributes data at sites where the straw is removed. The best modeling coefficients for stalk yield were obtained using soil data from the 0–20 cm layer. The straw removal induced reduction in soil C, chemical and physical quality (0–5 cm) depending on site, but did not result in lower yields. The research findings provide important information that will lead to sustainable bioenergy production in Brazil.

Remote Sensing-Based Yield Forecasting for Sugarcane (Saccharum officinarum L.) Crop in India

Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. This current study explored a remote sensing-based approach of predicting sugarcane yield, at district level, using Vegetation Condition Index (VCI), under the FASAL programme of the Ministry of Agriculture & Farmers’ Welfare. 13-years’ historical database (2003–2015) of NDVI was used to derive the VCI. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of June to second fortnight of October (peak growing period) were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of sugarcane over 52 major sugarcane-growing districts in five states of India. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level sugarcane yield. Though there was variation in model performance in different states, overall, the study showed the usefulness of VCI, which can be used as an input for operational sugarcane yield forecasting.

Application of AquaCrop model to predict sugarcane yield under the climate change impact: A case study of Son Hoa district, Phu Yen province in Vietnam

This work evaluates the climate change impact on sugarcane yield and proposes the solution to shift the seasonal calendar for matching with sugarcane in the Son Hoa District, Phu Yen Province of Vietnam. Future climate change scenarios for time periods in the 2020s, 2055s and 2090s corresponding to low emission scenario RCP4.5 and high emission scenario RCP8.5 were selected to evaluate sugarcane yield in the study area. Simulated results showed that climate change impact would increase sugarcane yield from 1.73 to 8.26% for both RCP4.5 and RCP8.5 scenarios. The increase of sugarcane yield can be significantly improved when the crop planting calendar will be shifted 40 days delay and sugarcane yield will increase from 1.12 to 8.42 t/ha for both RCP4.5 and RCP8.5 scenarios.

Sugarcane yield and plant nutrient response to sulfur-amended Everglades histosols

ABSTRACTHigh soil pH causes leaf nutrient deficiencies and reduces sugarcane yield. Soil pH in Florida histosols has been increasing as these soils subside and depth to limestone is decreased. A factorial experiment of four sulfur (S) rates and three added calcium carbonate (CaCO3) levels in soil was designed to determine S-amendment effectiveness in reducing pH and increasing nutrient availability in sugarcane as calcium (Ca) carbonate levels were increased. Sulfur-amendment and increased CaCO3 level had limited effects on yield and leaf nutrient concentrations during the growing season. Most leaf nutrients were within optimum range except nitrogen (N), phosphorus (P), iron (Fe), and manganese (Mn). Unexpected increases in Mn concentrations with added CaCO3 were associated with reducing conditions due to increased soil bulk density. High soil pH caused Mn deficiencies in the plants. Soil pH, P and Mn concentrations were important factors in predicting sugarcane yield.

A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region

Quantifying sugarcane production is critical for a wide range of applications, including crop management and decision making processes such as harvesting, storage, and forward selling. This study explored a novel model for predicting sugarcane yield in Bundaberg region from time series Landsat data. From the freely available Landsat archive, 98 cloud free (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31st (2001-2015) were sourced for this study. The images were masked using the field boundary layer vector files of each year and the GNDVI was calculated. An analysis of average green normalized difference vegetation index (GNDVI) values from all sugarcane crops grown within the Bundaberg region over the 15 year period identified the beginning of April as the peak growth stage and, therefore, the optimum time for satellite image based yield forecasting. As the GNDVI is an indicator of crop vigor, the model derived maximum GNDVI was regressed against historical sugarcane yield data, which showed a significant correlation with R2 = 0.69 and RMSE = 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible and a modest technique to predict sugarcane yield in Bundaberg region.

Determining the Best Optimum Time for Predicting Sugarcane Yield Using Hyper-Temporal Satellite Imagery

Hyper-temporal satellite imagery provides timely up to date and relatively accurate information for the management of crops. Nonetheless models which use high time series satellite data for sugarcane yield estimation remain scant. This study determined the best optimum time for predicting sugarcane yield using the normalized difference vegetation index (NDVI) derived from SPOT-VEGETATION images. The study used actual yield data obtained from the mill and related it to NDVI of several two-month periods of integration spread along the sugarcane growing cycle. Findings were in agreement with results of previous studies which indicated that the best acquisition period of satellite images for the assessment of sugarcane yield is about 2 months preceding the beginning of harvest. Overall, of the five years tested to determine the relationship between actual yield and integrated NDVI, three years showed a significant positive relationship with a highest r2 value of 85%. The study however warrants further investigation to improve and develop accurate operational sugarcane yield estimation models at the local level given that other years had weak results. Such hybrid models may combine different vegetation indexes with agro-meteorological models which take into account broader crop’s physiological, growth demands, and soil management which are equally important when predicting yield.

Estimating Sugarcane Yield Potential Using an In-Season Determination of Normalized Difference Vegetative Index

Estimating crop yield using remote sensing techniques has proven to be successful. However, sugarcane possesses unique characteristics; such as, a multi-year cropping cycle and plant height-limiting for midseason fertilizer application timing. Our study objective was to determine if sugarcane yield potential could be estimated using an in-season estimation of normalized difference vegetative index (NDVI). Sensor readings were taken using the GreenSeeker® handheld sensor from 2008 to 2011 in St. Gabriel and Jeanerette, LA, USA. In-season estimates of yield (INSEY) values were calculated by dividing NDVI by thermal variables. Optimum timing for estimating sugarcane yield was between 601–750 GDD. In-season estimated yield values improved the yield potential (YP) model compared to using NDVI. Generally, INSEY value showed a positive exponential relationship with yield (r2 values 0.48 and 0.42 for cane tonnage and sugar yield, respectively). When models were separated based on canopy structure there was an increase the strength of the relationship for the erectophile varieties (r2 0.53 and 0.47 for cane tonnage and sugar yield, respectively); however, the model for planophile varieties weakened slightly. Results of this study indicate using an INSEY value for predicting sugarcane yield shows potential of being a valuable management tool for sugarcane producers in Louisiana.