Isohydric and anisohydric characterisation of vegetable crops. The classification of vegetables by their physiological responses to water stress

Research on the physiological response of crop plants to drying soils and subsequent water stress has grouped plant behaviours as isohydric and anisohydric. Drying soil conditions, and hence declining soil and root water potentials, cause chemical signals—the most studied being abscisic acid (ABA)—and hydraulic signals to be transmitted to the leaf via xylem pathways. Researchers have attempted to allocate crops as isohydric or anisohydric. However, different cultivars within crops, and even the same cultivars grown in different environments/climates, can exhibit both response types. Nevertheless, understanding which behaviours predominate in which crops and circumstances may be beneficial. This paper describes different physiological water stress responses, attempts to classify vegetable crops according to reported water stress responses, and also discusses implications for irrigation decision-making.

Cadmium stress alters the redox reaction and hormone balance in oilseed rape (Brassica napus L.) leaves

In order to understand the physiological response of oilseed rape (Brassica napus L.) leaves to cadmium (Cd) stress and exploit the physiological mechanisms involved in Cd tolerance, macro-mineral and chlorophyll concentrations, reactive oxygen species (ROS) accumulation, activities of enzymatic antioxidants, nonenzymatic compounds metabolism, endogenous hormonal changes, and balance in leaves of oilseed rape exposed to 0, 100, or 200 μM CdSO4 were investigated. The results showed that under Cd exposure, Cd concentrations in the leaves continually increased while macro-minerals and chlorophyll concentrations decreased significantly. Meanwhile, with increased Cd stress, superoxide anion (O 2 • − ) production rate and hydrogen peroxide (H2O2) concentrations in the leaves increased significantly, which caused malondialdehyde (MDA) accumulation and oxidative stress. For scavenging excess accumulated ROS and alleviating oxidative injury in the leaves, the activity of enzymatic antioxidants, such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), was increased significantly at certain stress levels. However, with increased Cd stress, the antioxidant enzyme activities all showed a trend towards reduction. The nonenzymatic antioxidative compounds, such as proline and total soluble sugars, accumulated continuously with increased Cd stress to play a long-term role in scavenging ROS. In addition, ABA levels also increased continuously with Cd stress while ZR decreased and the ABA/ZR ratio increased, which might also be providing a protective role against Cd toxicity.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.