The aims of this study were: (1) the study of cadmium (Cd) accumulation and toxicity in different castor cultivars (Ricinus communis L.); (2) to investigate changes in antioxidant enzymatic activities and the subcellular distribution of Cd in young and old leaves from two different castor cultivars, after exposure to two different Cd concentrations, and explore the underlying mechanism of Cd detoxification focusing on antioxidant enzymes and subcellular compartmentalization. The Cd concentration, toxicity, and subcellular distribution, as well as superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) activities were measured in Zibo-3 and Zibo-9 cultivars after exposure to two different concentrations of Cd (2mg/L and 5mg/L) for 10 days. This research revealed Cd accumulation characteristics in castor are root>stem>young leaf>old leaf. Castor tolerance was Cd dose exposure and the cultivars themselves dependent. Investigation of subcellular Cd partitioning showed that Cd accumulated mainly in the heat stable protein (HSP) and cellular debris fractions, followed by the Cd rich granule (MRG), heat denatured protein (HDP), and organelle fractions. With increasing Cd concentration in nutrient solution, the decreased detoxified fractions (BDM) and the increased Cd-sensitive fractions (MSF) in young leaves may indicate the increased Cd toxicity in castor cultivars. The BDM-Cd fractions or MSF-Cd in old leaves may be linked with Cd tolerance of different cultivars of castor. The antioxidant enzymes that govern Cd detoxification were not found to be active in leaves. Taken together, these results indicate Cd tolerance and toxicity in castor can be explained by subcellular partitioning.

Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to identify the timing of phenological events from sequences of Landsat NDVI and derive phenological and seasonal climate metrics. Using data from a 17,000 ha study area, we investigated the relationships between the metrics and yield over 17 years from 2003 to 2019. We compared six statistical and machine learning models for estimating yield: multiple linear regression, mixed effects models, generalised additive models, random forests, support vector regression using radial basis functions and deep learning neural networks. We used a 50-50 train-test split on paddock-years where 50% of paddock-year combinations were randomly selected and used to train each model and the remaining 50% of paddock-years were used to assess the model accuracy. Using only phenological metrics, accuracy was highest using a linear mixed model with a random effect that allowed the relationship between integrated NDVI and yield to vary by year $({\mathrm{R}}^2$ = 0.67, MAE = 0.25 t ha⁻¹, RMSE = 0.33 t ha⁻¹, NRMSE = 0.25). We quantified the improvements in accuracy when seasonal climate metrics were also used as predictors. We identified two optimal models using the combined phenological and seasonal climate metrics: support vector regression and deep learning models (${\mathrm{R}}^2$ = 0.68, MAE = 0.25 t ha⁻¹, RMSE = 0.32 t ha⁻¹, NRMSE = 0.25). While the linear mixed model using only phenological metrics performed similarly to the nonlinear models that are also seasonal climate metrics, the nonlinear models can be more easily generalised to estimate yield in years for which training data are unavailable. We conclude that long-term hindcasts of wheat yield in fields, at 30 m spatial resolution, can be produced using remotely sensed phenology from Landsat NDVI, climate data and machine learning.

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.