Tons Of Cane Per Hectare Research Articles

Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ) and for TCH ( ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

Use of continuous genotypes for genomic prediction in sugarcane.

Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo-diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele-dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non-parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%-7% for CCS and Fiber. The pseudo-diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc-cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non-additive effects might influence these traits. The prevalence of low-dosage markers in the study may have limited the benefits of approximating allele-dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo-diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies.

Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

The use of UAS-based high throughput phenotyping (HTP) to assess sugarcane yield

A sensor mounted on an unmanned aerial system (UAS) may enable breeding programs’ selection efficiency. The objectives of this study were to investigate the use of UAS with red, green, and blue (RGB) camera to assess sugarcane yield and to investigate the direct and indirect influences of canopy features on cane yield of sugarcane. A trial was conducted from 2019 to 2021 at the Texas A&M AgriLife Research and Extension Center in Weslaco, Texas, and arranged in a complete randomized block design, consisting of 7 genotypes with 4 replications. Seven UAS image acquisitions were performed at the plant cane stage using an RGB sensor. At the first ratoon stage, five flights were conducted using RGB and multispectral sensors. Ground measurements were obtained, including pol, tons of sugar per hectare (TSH), and tons of cane per hectare (TCH). The results showed that the correlation of canopy features with pol at the elongation phase was poor, compared to those acquired at the maturity phase. Likewise, the most significant correlations between canopy features with TSH and TCH were found in the late elongation to maturity phase. In addition, a good agreement of the validation dataset between the observed and predicted pol (R2 0.58, RMSE 0.77%), TSH (R2 0.68, RMSE 2.24 Mg ha−1), and TCH (R2 0.66, RMSE 13.64 Mg ha−1) was observed. The canopy height model (CHM) and Normalized Difference Vegetation Index (NDVI) had the highest direct effect on TCH. Therefore, assessing sugarcane yield using UAS-based imagery looks promising for High Throughput Phenotyping (HTP) in sugarcane breeding programs.

Yields of Promising Sugarcane Clones under Three Different Planting Arrangements

A field experiment was conducted at Kediri, East Java, to examine the influence of row spacing on the yield of five promising clones from August 2018-June 2020. The investigation was of a Split-Plot design with three replications. The main plot consisted of three planting arrangements: SR, single row spacing at 110 cm, DR1, double rows spacing 50+135 cm, and DR2, double rows spacing 50+170 cm. Sub-plots consisted of five promising clones and one check variety: MLG 5, MLG 9, MLG 14, MLG 52, MLG 55, and PS 881. The results suggest no overall significant effect of planting arrangement on sugar yield, though clones may vary. There are significant differences in yield between some clones. Wider rows, DR2, show the highest yield in tonnes of cane per hectare (TCH) in PC (153.94 t/ha) with a slight decrease (14%) in RC-1 due to reduced plant population. Sugar yield in tonnes of sugar per hectare (TSH) in various planting arrangements ranges from 12.26-13.99 t/ha in PC and 10.68-11.93 t/ha in RC-1. MLG 9 can be released as a new variety with high cane and sugar yields.

Novel Tools for Adjusting Spatial Variability in the Early Sugarcane Breeding Stage.

The detection of spatial variability in field trials has great potential for accelerating plant breeding progress due to the possibility of better controlling non-genetic variation. Therefore, we aimed to evaluate a digital soil mapping approach and a high-density soil sampling procedure for identifying and adjusting spatial dependence in the early sugarcane breeding stage. Two experiments were conducted in regions with different soil classifications. High-density sampling of soil physical and chemical properties was performed in a regular grid to investigate the structure of spatial variability. Soil apparent electrical conductivity (ECa) was measured in both experimental areas with an EM38-MK2® sensor. In addition, principal component analysis (PCA) was employed to reduce the dimensionality of the physical and chemical soil data sets. After conducting the PCA and obtaining different thematic maps, we determined each experimental plot’s exact position within the field. Tons of cane per hectare (TCH) data for each experiment were obtained and analyzed using mixed linear models. When environmental covariates were considered, a previous forward model selection step was applied to incorporate the variables. The PCA based on high-density soil sampling data captured part of the total variability in the data for Experimental Area 1 and was suggested to be an efficient index to be incorporated as a covariate in the statistical model, reducing the experimental error (residual variation coefficient, CVe). When incorporated into the different statistical models, the ECa information increased the selection accuracy of the experimental genotypes. Therefore, we demonstrate that the genetic parameter increased when both approaches (spatial analysis and environmental covariates) were employed.

Accuracy of genomic prediction of complex traits in sugarcane.

Complex traits in sugarcane can be accurately predicted using genome-wide DNA markers. Genomic single-step prediction is an attractive method for genomic selection in commercial breeding programs. Sugarcane breeding programs have achieved up to 1% genetic gain in key traits such as tonnes of cane per hectare (TCH), commercial cane sugar (CCS) and Fibre content over the past decades. Here, we assess the potential of genomic selection to increase the rate of genetic gain for these traits by deriving genomic estimated breeding values (GEBVs) from a reference population of 3984 clones genotyped for 26K SNP. We evaluated the three different genomic prediction approaches GBLUP, genomic single step (GenomicSS), and BayesR. GenomicSS combining pedigree and SNP information from historic and recent breeding programs achieved the most accurate predictions for most traits (0.3-0.44). This method is attractive for routine genetic evaluation because it requires relatively little modification to the existing evaluation and results in breeding value estimates for all individuals, not only those genotyped. Adding information from early-stage trials added up to 5% accuracy for CCS and Fibre, but 0% for TCH, reflecting the importance of competition effects for TCH. These GEBV accuracies are sufficiently high that, combined with the right breeding strategy, a doubling of the rate of genetic gain could be achieved. We also assessed the flowering traits days to flowering, gender and pollen viability and found high heritabilities of 0.57, 0.78 and 0.72, respectively. The GEBV accuracies indicated that genomic selection could be used to improve these traits. This could open new avenues for breeders to manage their breeding programs, for example, by synchronising flowering time and selecting males with high pollen viability.

COMPARATIVE OF AGROECOLOGICALLY FERTILIZERS WITH MINERAL FERTILIZER IN SECOND CUT SUGARCANE IN THE REGION OF THE SOUTHWEST STATE OF GOIÁS, BRAZIL

This work evaluated the second cut of the technological variables "plant biometrics" of sugarcane culture using different fertilizers and different doses of organomineral fertilizers in the southwestern region of Goiás, Brazil. The experiment was conducted in the 2017/2018 harvest, in Mineiros, state of Goiás, Brazil. Initial and final plant population, tons of cane per hectare, total recovered sugar, plant height, stalk diameter, and internodes number were evaluated. There were eight procedures with three different dosages of organominerals, three dosages of poultry beds, one chemical fertilizer, and control distributed in randomized blocks with four repetitions. It can be concluded that the poultry bed is excellent for keeping technological variables tested in the best achieved means, with the two main parameters tons of cane per hectare (TCH) and total recovered sugar (TRS) being statistically equal when compared to mineral fertilizer.

Can statistical learning models make early selection among sugarcane families easier and still efficient?

AbstractThe selection of genotypes at the early stages is one of the main challenges facing sugarcane (Saccharum officinarum L.) breeding programs. The present work aimed to compare classification techniques, namely, logistic regression (LR), k‐nearest neighbor (KNN), random forests (RF), and support vector machine (SVM) against the selection among families of sugarcane via artificial neural networks (ANN) and via a procrefers to the families incorrectly selected byedure based on the weighing of the plots. The data used in this work were obtained from 110 families. In the families, the number of stalks (NS), stalk diameter (SD), and stalk height (SH) were collected, in addition to the actual yield, expressed in tons of cane per hectare (TCH). We considered the NS, SD, and SH as explanatory variables for the training of the classifiers. The response used was the indicator Y = 0 if the family is not selected via TCH or Y = 1 otherwise. To increase the efficiency in training, we produced synthetic data based on the simulation of NS, SD, SH, and TCH values. Two models were also considered: a full model with all the predictors and a reduced model without the SH. We used the apparent error rate (AER) and the true positive rate (TPR) for the evaluation of the classifiers. All classifiers present low values for the AER and high values for the TPR in both models. The best performance was observed in the SVM. The reduced model should be preferred, since its performance is very close to that of the full model and its operation is more straightforward.

Metabolic changes in the developing sugarcane culm associated with high yield and early high sugar content.

Sugarcane, with its exceptional biomass and sugar yield, has a high potential for the production of bioenergy, biomaterials, and high‐value products. Currently, the link between metabolic changes in the developing internodes in sugarcane and final yield and sugar characteristics is not well understood. In order to investigate these correlations, 1,440 internodes were collected and combined to generate a set of 360 samples across 24 sugarcane cultivars at five different developmental stages. A combination of metabolome profiling and trait co‐expression analyses were conducted to reveal the interaction between the metabolome and essential agronomic traits, including Brix (total sugar), polarity (sucrose content), purity (sucrose purity), commercially extractable sucrose, fiber, and tons of cane per hectare (TCH). Metabolomic analysis revealed significant differences in metabolic patterns mainly correlated with developmental stage. Hierarchical clustering of genotypes and traits revealed clear partitioning of groups of early‐, mid‐ and late‐season sugar content, with secondary segregation by the yield trait, TCH, and fiber content. The study identified co‐expression and specific metabolites associated with metabolic pathways correlated with Brix and fiber content. Knowledge of the correlation between co‐expressed metabolites and diverse agronomic traits will allow more deliberate selection of genotypes for early or late sugar development and fiber content and biomass yield.

Seleção de genótipos de cana-de-açúcar em três épocas de colheita

Objetivou-se com este trabalho a seleção de genótipos de cana-de-açúcar, para três épocas de colheita, a partir da utilização de técnicas multivariadas. Os experimentos foram conduzidos na área agrícola da Usina União e Indústria, município de Primavera, Pernambuco. Utilizou-se o delineamento em blocos completos casualizados com quatro repetições. Os caracteres agroindustriais avaliados foram: toneladas de pol por hectare (TPH), toneladas de cana por hectare (TCH), fibra (FIB), pol % corrigida (PCC), pureza (PZA), teor de sólidos solúveis (BRIX) e açúcar total recuperável (ATR). Realizaram-se quatro testes multivariados, testes de efeitos entre sujeitos e comparações múltiplas. Realizou-se ainda a análise de componentes principais visando a discriminação dos genótipos quanto ao desempenho agroindustrial nas épocas de colheita. A análise de variância multivariada (MANOVA) revelou que há efeito significativo das épocas de colheita sobre os caracteres dos genótipos. Os testes de efeitos entre sujeitos revelaram, por sua vez, diferenças significativas entre os caracteres nas respectivas épocas de colheita, indicando a presença de variabilidade genética e a possibilidade de seleção de materiais produtivos para as três épocas que correspondem a três segmentos distintos de maturação. O genótipo RB92579 se destaca quanto a produtividade e maior rentabilidade econômica, podendo ser cultivado e colhido em qualquer segmento de maturação.

Effects of Nitrogen Fertilization and Seed Piece Applied Fungicides on Establishment, Tiller Dynamics, and Sucrose Yields in Successively Planted Sugarcane

Sugarcane (Saccharum spp.) successive planting causes 25–30% yield reduction in comparison to fallow or rice rotation planting in a three-year production cycle on Florida Histosols. Field experiments were established to manage the yield losses associated with successive planting through nitrogen fertilization and seed piece application of fungicides in plant and first ratoon crops each at two sites. Nitrogen fertilization treatments included 0 (N0), 50 (N50), and 100 (N100) kg ha−1 applied in furrows at the time of planting, and one split application (N50+50) with 50 kg ha−1 applied at planting and 50 kg ha−1 applied at 90 days after planting as side-dress. Fungicides treatments were mancozeb at 2.5 kg a.i. (active ingredient) ha−1, mefenoxam at 0.57 kg a.i. ha−1, and azoxystrobin at 0.30 kg a.i. ha−1 applied to seed cane pieces laid in the furrows at planting. Nitrogen fertilization showed increasing trends of the tiller and millable stalks production in plant and ratoon crops. N response varied with the time of ratooning. Overall, N50+50 produced greater tons of cane per hectare (TCH) and tons of sucrose per hectare (TSH) compared to other N treatments in plant crop and late season ratoon crop (ratooned in March). N100 treatment enhanced tillering and TCH in December ratooned crop. In 2016 plant crop, mefenoxam produced higher TCH than others, but no carryover effects were observed in ratoon crops. Both nitrogen fertilization and fungicides seem to be promising cultural practices to minimize yield losses in successive sugarcane planting in Histosols.

SOIL PHYSICAL ATTRIBUTES AND PRODUCTION COMPONENTS OF SUGARCANE CULTIVARS IN CONSERVATIONIST TILLAGE SYSTEMS

Machine traffic and conventional tillage can cause structural degradation of the soil, affecting the physical attributes and, consequently, the production and longevity of the sugarcane field. The objective of this study was to evaluate the production components of sugarcane cultivars (RB965902, RB985476, RB966928, RB855156, RB975201, RB975242, RB036066 and RB855536) and physical attributes of a Dystroferric Red Latosol (Oxisol) under no-tillage and reduced-tillage systems. The experiment was conducted using a completely randomized experimental design in an 8 × 2 factorial arrangement, with four replicates. The soil attributes evaluated were density, total porosity, macroporosity, microporosity and penetration resistance. The production components evaluated were stalk length and diameter, number of tillers (NT), tons of cane per hectare (TCH), sucrose content (Pol), tons of sucrose per hectare (TPH), soluble solids content (Brix), total recoverable sugars (TRS) and plant fiber. Cultivars RB965902, RB966928, RB855156 and RB985476 have productivity and technological attributes that are superior to the other cultivars, whereas these are lower for RB036066 in both soil tillage systems. Cultivars RB966928, RB855156, RB975242 and RB855536 exhibited higher TCH and TPH in the no-tillage system, and all cultivars displayed equal or higher performances than those observed in the reduced-tillage system. In the no-tillage system, which exhibits lower penetration resistance values between wheel tracks and in the subsurface layer of the planting row (0.20–0.40 m), cultivars RB985476 and RB975242 had a higher NT, and RB855156 had higher stalk lengths and NT.

Repeatibility of agroindustrial characters in sugarcane in different harvest cycles

n commercial cultivation of sugarcane, knowledge about the repetition of agroindustrial characters is essential to identify long-lived genotypes in production cycles, which when selected, will contribute to the significant increase in productivity. This work evaluated the agroindustrial performance of 16 sugarcane genotypes in the sugarcane microregion Litoral Norte of Pernambuco in four harvest cycles and the regularity in the repetition of characters. The experiment was conducted in the agricultural area of Sao Jose sugar mill, Igarassu, state of Pernambuco, Brazil. The experiment was carried out following a randomized block design with four replications. The variables evaluated were: tons of POL per hectare (TPH), tons of cane per hectare (TCH), fiber (FIB), adjusted POL% (PCC), soluble solids content (BRIX), and total recoverable sugar (TRS). The variance analysis detected significant differences among the genotypes along the four harvest seasons, indicating genetic variability and possibility of success in the selection of superior genotypes. Estimates of repeatability coefficient point to regularity in the repetition of agro-industrial characteristics allowing to identify genotypes with the highest longevity. The genotypes SP79-1011, RB863129, RB92579, RB813804, RB982559 e RB982613 presented best agroindustrial performance, and two evaluations based on TPH and TCH characters are enough to select superior genotypes with 90% predictability of their actual values.

Analysis of multienvironment trials (MET) in the sugarcane breeding program of R\xe9union Island

The sugarcane variety development program on Réunion Island is dedicated to an industry that encompasses numerous different agroclimatic production zones. The objective of this study is to characterize in detail the final selection stage of this program, consisting of multienvironment trials (MET) at seven representative locations, considering the genotypic response in terms of tonnes of cane per hectare (TCH), estimable recoverable sugar (ERS), fiber content (FIB), and an economic index (EI). Data from four recent variety series tested between one and three crop years were used. Each trait revealed a significant genotype × location (GL) interaction, always higher than the genotype × crop year (GC) interaction, indicating that testing genotypes across locations is more important than testing for ratooning ability. Broad-sense heritability (H) at MET level was relatively high for FIB compared with EI, while ERS and TCH exhibited intermediate H values. Genotype main effect plus genotype × environment (GGE) biplot analysis applied to a balanced set of genotypes tested over two crop years across all environments permitted reliable visualization at a glance of (i) the level of proximity between genotypes or environments, and (ii) the performance of varieties in each environment and their stability across all of them. No redundancy between any pair of environments was found for the most important selection trait (EI). These results confirm the relevance of a selection strategy firstly oriented toward selecting sugarcane genotypes for local adaptations with the objective of enhancing the mean productivity of the whole cane industry.

Selecting Parents, Families, and Clones to Obtain Energy Cane

Core Ideas The results presented the possibility of selection of energy cane clones. Develop energy cane clones with high fiber contents, involving Saccharum spontaneum accesses. We found clones with high fiber content and high sucrose levels. Sugarcane (Saccharum spp.) is a crop with a high potential for biomass production. Sugarcane residue and bagasse can be used in the production of ethanol and electricity. The genetic breeding program of the Inter‐University Network for the Development of the Sugarcane Industry (RIDESA) initiated a hybridization program that includes species of the Saccharum complex. The objective of this study was to evaluate the genetic diversity in selected segregating populations, select the best families and clones, and identify potential parents for inclusion in the next phase of the energy cane breeding program to develop energy cane cultivars. In the present study, 50 full‐sib families and 15 half‐sib families were crossed. The traits evaluated were: the mean number of stalks per plant (NS), the mean stalk weight, fiber content, % (FIB%), and sucrose content, % (SC%). The industrial traits evaluated were: tonnes of cane per hectare (TCH), tonnes of fiber per hectare (TFH), and tonnes of sucrose per hectare (TSH). The best‐performing parents were RB867515, RB93509, Co285, B70710, MEX68‐200, IM76‐228, and IM76‐229. The heritability (0.49–0.91) and accuracy values (0.70–0.95) for the parameters TCH, FIB, and SC indicated that the predicted genotypic means and observed values were highly correlated, which enabled the efficient selection of the best energy cane families. The genetic variability in the segregating populations and the identification of clones with valuable traits indicate the possibility of using superior genotypes in new crosses and future hybridizations and developing new strategies to evaluate and select energy cane families.

Field Performance of Transgenic Sugarcane Lines Resistant to Sugarcane Mosaic Virus.

Sugarcane mosaic disease is mainly caused by the sugarcane mosaic virus (SCMV), which can significantly reduce stalk yield and sucrose content of sugarcane in the field. Coat protein mediated protection (CPMP) is an effective strategy to improve virus resistance. A 2-year field study was conducted to compare five independent transgenic sugarcane lines carrying the SCMV-CP gene (i.e., B2, B36, B38, B48, and B51) with the wild-type parental clone Badila (WT). Agronomic performance, resistance to SCMV infection, and transgene stability were evaluated and compared with the wild-type parental clone Badila (WT) at four experimental locations in China across two successive seasons, i.e., plant cane (PC) and 1st ratoon cane (1R). All transgenic lines derived from Badila had significantly greater tons of cane per hectare (TCH) and tons of sucrose per hectare (TSH) as well as lower SCMV disease incidence than those from Badila in the PC and 1R crops. The transgenic line B48 was highly resistant to SCMV with less than 3% incidence of infection. The recovery phenotype of transgenic line B36 was infected soon after virus inoculation, but the subsequent leaves showed no symptoms of infection. Most control plants developed symptoms that persisted and spread throughout the plant with more than 50% incidence. B48 recorded an average of 102.72 t/ha, which was 67.2% more than that for Badila. The expression of the transgene was stable over many generations with vegetative propagation. These results show that SCMV-resistant transgenic lines derived from Badila can provide resistant germplasm for sugarcane breeding and can also be used to study virus resistance mechanisms. This is the first report on the development and field performance of transgenic sugarcane plants that are resistant to SCMV infection in China.

Identification of commercial blocks of outstanding performance of sugarcane using data mining

In order to achieve more efficient agricultural production systems, studies relating to the patterns of influence factors on commercial blocks of outstanding performance can be performed to assist management practices. The performance is considered to be the difference between the yield of a given block and the average yield of the homogeneous group that it belongs to. The methods available to identify these outstanding blocks are usually subjective. The aim of this study was to propose an objective and repeatable approach to identify outstanding performance blocks. The proposed approach consisted of performance determination, using regression trees, and the classification of these blocks by k-means clustering. This approach was illustrated using a sugarcane model. The main factors influencing the tonnes of cane per hectare (TCH) and total recoverable sugar (TRS) yields were found to be crop age and water availability during ripening, respectively. These were used to create potential yield groups, and blocks with high and low performance were identified. The proposed approach was found to be valid in the identification of outstanding sugarcane blocks, and it can be applied to different crops or in the context of precision agriculture.

Effect of Micronutrients Soil Supplementation on Sugarcane in Different Production Environments: Cane Plant Cycle

Fertilization of sugarcane (Saccharum spp.) with micronutrients is not common and this can affect crop yield. Research results of micronutrients in sugarcane in Brazil are still scarce and inconclusive. The aim of the present study was to assess the sugarcane crop response to Cu, Zn, Mn, Mo, and B fertilization in different sugarcane production environments in the state of São Paulo, Brazil. An experiment was conducted at 11 locations. The treatments consisted of the application of fixed micronutrient doses only to the planting furrow as follows: control, Zn (10 kg ha−1), Mn (10 kg ha−1), Cu (10 kg ha−1), B (3 kg ha−1), Mo (2 kg ha−1), and complete (Zn +Mn + Cu + B +Mo). The design consisted of a randomized block with five replicates. The leaf content, tillering, sugarcane yield, and total recoverable sugar were determined in cane plant. The responses varied with location, soil type, climate, and variety. Positive responses to micronutrients application were observed in all parameters studied with the exception of total recoverable sugar. The sugarcane yield increased significantly with the application of micronutrients in the following order of magnitude: Zn > Mo = Mn > Cu > B = Complete, with yield gain of 18, 12, 12, 10, 9, and 9 tonnes of cane per hectare (TCH), respectively. Sugarcane responded positively to micronutrient, especially Zn fertilization, demonstrating that this practice may be economically viable and increase the productivity of Brazilian sugarcane fields, making agribusiness sugarcane more sustainable. Micronutrients can increase sugarcane yield. Zinc is the principal micronutrient to sugarcane producer. The application of high doses of micronutrients may increase the productivity of sugarcane Brazilians.

Simulated Individual Best Linear Unbiased Prediction versus Mass Selection in Sugarcane Families

The purpose of this study was to compare the method BLUPIS (best linear unbiased prediction individual simulated) with mass selection in terms of efficiency in identifying the best genotypes in sugarcane (Saccharum officinarum L.) families. Mass selection was performed by two breeders with 25 yr of experience. The BLUPIS procedure selected families with higher means for tons of cane per hectare (TCH) than the overall mean. The number of plants selected per family was calculated by , where ĝ indicates the genotypic value of the kth family; ĝ the genotypic value of the best family; and nj is equal to the number of plants selected in the best family, determined as nj = 45 in this study. Out of 20 best clones forwarded to the third test phase (T3), BLUPIS selected all in the first test phase (T1) and mass selection only two. Therefore, 100% of the clones in the second test phase (T2) had been selected by BLUPIS. The BLUPIS was most efficient in detecting the best genotypes, since all clones that were promoted up to phase T3 were descendants from the best families. The BLUPIS method should be applied in sugarcane breeding programs to ensure the selection of the best genotypes.