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Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally associated with surveys and censuses. Fraction of absorbed photosynthetically active radiation (fAPAR), chlorophyll concentration (CI) and normalized difference vegetation (NDVI) indices were used in crop monitoring, but their comparative performances in sorghum monitoring is lacking. This work aimed therefore at closing this gap by evaluating the performance of machine learning modelling of in-season sorghum biomass yields based on Sentinel-2-derived fAPAR and simpler high-throughput optical handheld meters-derived NDVI and CI calculated from sorghum plants reflectance. Bayesian ridge regression showed good cross-validated performance, and high reliability (R2 = 35%) and low bias (mean absolute prediction error, MAPE = 0.4%) during the validation step. Hand-held optical meter-derived CI and Sentinel-2-derived fAPAR showed comparable effects on machine learning performance, but CI outperformed NDVI and was therefore considered as a good alternative to Sentinel-2's fAPAR. The best times to sample the vegetation indices were the months of June (second half) and July. The results obtained in this work will serve several purposes including improvements in plant breeding, farming management and sorghum biomass yield forecasting at extension services and policy making levels. [ABSTRACT FROM AUTHOR]

The purpose of this work was to assess biomass and grain yields, path modelling of yield components, and perenniality in 97 Sorghum bicolor x S. halepense (SB x SH) lines, and compare their biomass production to a series of 38 historic (1987–2015) biomass SB trials. Perenniality was evaluated as rhizome overwintering, while other traits were scored using standard procedures. Contrary to SB, several SB x SH lines developed rhizomes and overwintered, depending mostly upon increased dosage of SH genome in SB background. A few backcross-derived SB x SH lines overwintered, indicating that perenniality can be introgressed in sorghum using backcrossing approach. SB x SH lines were competitive in terms of grain and aboveground dry biomass yields compared to SB. SB x SH selections outperformed or were comparable to the best commercial biomass SB hybrid checks, and ranked among the best selections from the historic trials. Grain yield in SB x SH lines depended mainly on number of culms, whereas biomass yields depended mainly on plant height, dry mass and fiber mass fractions of dry material, and number of culms. We identified superior dual purpose SB x SH genotypes producing 5–11 and 38–45 t ha −1 of grain and aboveground biomass, respectively, most of which displayed satisfactory overwintering rates (56–100%) and high levels of fiber (61–69%) mass fraction. These lines represent high energy sorghums suitable for biofuel and food production. The best SB x SH crosses for developing perennial biomass and grain sorghums were represented by perennial/perennial, annual/perennial/perennial, and annual/perennial combinations. [ABSTRACT FROM AUTHOR]

Sorghum is one of the world's major crops, expresses traits for resilience to climate change, and can be used for several purposes including food and clean fuels. Multiple-trait genomic prediction and selection models were implemented using genotyping-by-sequencing single nucleotide polymorphism markers and phenotypic data information. We demonstrated for the first time the efficiency genomic selection modelling of index selection including biofuel traits such as aboveground biomass yield, plant height, and dry mass fraction of the fresh material. This work also sheds light, for the first time, on the promising potential of using the information from the populations grown from seed to predict the performance of the populations regrown from the rhizomes—even two winter seasons after the original trial was sown. Genomic selection modelling of the optimum index selection including the three traits of interest (plant height, aboveground dry biomass yield, and dry mass fraction of fresh mass material) was the most promising. Since the plant characteristics evaluated herein are routinely measured in cereal and other plant species of agricultural interest, it can be inferred that the findings can be transferred in other major crops. [ABSTRACT FROM AUTHOR]

Sorghum crop is grown under tropical and temperate latitudes for several purposes including production of health promoting food from the kernel and forage and biofuels from aboveground biomass. One of the concerns of policy-makers and sorghum growers is to cost-effectively predict biomass yields early during the cropping season to improve biomass and biofuel management. The objective of this study was to investigate if Sentinel-2 satellite images could be used to predict within-season biomass sorghum yields in the Mediterranean region. Thirteen machine learning algorithms were tested on fortnightly Sentinel-2A and Sentinel-2B estimates of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) in combination with in situ aboveground biomass yields from demonstrative fields in Italy. A gradient boosting algorithm implementing the xgbtree method was the best predictive model as it was satisfactorily implemented anywhere from May to July. The best prediction time was the month of May followed by May–June and May–July. To the best of our knowledge, this work represents the first time Sentinel-2-derived fAPAR is used in sorghum biomass predictive modeling. The results from this study will help farmers improve their sorghum biomass business operations and policy-makers and extension services improve energy planning and avoid energy-related crises. [ABSTRACT FROM AUTHOR]