Drought decreases crop productivity, with economic consequences for farmers. For soybean, drought particularly affects the reproductive phase. There is therefore a need for strategies that minimize drought effects, such as agronomic fortification with micronutrients. Here, we evaluated the mitigation of drought stress in soybean using composite formulations of three micronutrient nanoparticles, ZnO, B 2 O 3 , and CuO, and their salts: ZnSO 4 ·7H 2 O, H 3 BO 3 , and CuSO 4 · 5H 2 O, in a greenhouse. The micronutrients were soil or foliar applied 3 weeks after seed germination. Drought was imposed at 50% field moisture capacity. We measured parameters related to growth, yield, and nutrient uptake dynamics during 19 weeks. Results show that drought decreased soybean shoot growth by 27% and grain yield by 54%. Application of salt formulations to soil was more effective than foliar application, in mitigating drought stress. For foliar application, the effects of nanoparticles and salts were similar. 33%. Whereas micronutrient salts are known to reduce drought effects in plants, our findings demonstrate for the first time a novel use of micronutrient nanoparticles to boost crop performance and N and P uptake under drought stress.

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Zinc oxide nanoparticles (ZnO-NPs) hold promise as novel fertilizer nutrients for crops. However, their ultra-small size could hinder large-scale field application due to potential for drift, untimely dissolution or aggregation. In this study, urea was coated with ZnO-NPs (1%) or bulk ZnO (2%) and evaluated in wheat (Triticum aestivum L.) in a greenhouse, under drought (40% field moisture capacity; FMC) and non-drought (80% FMC) conditions, in comparison with urea not coated with ZnO (control), and urea with separate ZnO-NP (1%) or bulk ZnO (2%) amendment. Plants were exposed to ≤ 2.17 mg/kg ZnO-NPs and ≤ 4.34 mg/kg bulk-ZnO, indicating exposure to a higher rate of Zn from the bulk ZnO. ZnO-NPs and bulk-ZnO showed similar urea coating efficiencies of 74-75%. Drought significantly (p ≤ 0.05) increased time to panicle initiation, reduced grain yield, and inhibited uptake of Zn, nitrogen (N), and phosphorus (P). Under drought, ZnO-NPs significantly reduced average time to panicle initiation by 5 days, irrespective of coating, and relative to the control. In contrast, bulk ZnO did not affect time to panicle initiation. Compared to the control, grain yield increased significantly, 51 or 39%, with ZnO-NP-coated or uncoated urea. Yield increases from bulk-ZnO-coated or uncoated urea were insignificant, compared to both the control and the ZnO-NP treatments. Plant uptake of Zn increased by 24 or 8% with coated or uncoated ZnO-NPs; and by 78 or 10% with coated or uncoated bulk-ZnO. Under non-drought conditions, Zn treatment did not significantly reduce panicle initiation time, except with uncoated bulk-ZnO. Relative to the control, ZnO-NPs (irrespective of coating) significantly increased grain yield; and coated ZnO-NPs enhanced Zn uptake significantly. Zn fertilization did not significantly affect N and P uptake, regardless of particle size or coating. Collectively, these findings demonstrate that coating urea with ZnO-NPs enhances plant performance and Zn accumulation, thus potentiating field-scale deployment of nano-scale micronutrients. Notably, lower Zn inputs

The CO2 fertilization effect is a major source of uncertainty in crop models for future yield forecasts, but coordinated efforts to determine the mechanisms of this uncertainty have been lacking. Here, we studied causes of uncertainty among 16 crop models in predicting rice yield in response to elevated [CO2] (E-[CO2]) by comparison to free-air CO2 enrichment (FACE) and chamber experiments. The model ensemble reproduced the experimental results well. However, yield prediction in response to E-[CO2] varied significantly among the rice models. The variation was not random: models that overestimated at one experiment simulated greater yield enhancements at the others. The variation was not associated with model structure or magnitude of photosynthetic response to E-[CO2] but was significantly associated with the predictions of leaf area. This suggests that modelled secondary effects of E-[CO2] on morphological development, primarily leaf area, are the sources of model uncertainty. Rice morphological development is conservative to carbon acquisition. Uncertainty will be reduced by incorporating this conservative nature of the morphological response to E-[CO2] into the models. Nitrogen levels, particularly under limited situations, make the prediction more uncertain. Improving models to account for [CO2] × N interactions is necessary to better evaluate management practices under climate change.

Self-healing materials have received increased attention because of their automatic detecting and repairing damage function. In this paper, a novel self-assembly and self-healing bionanocomposite was developed as a coating material for controlled release fertilizers. This nanotechnology-enabled coating is environmentally friendly and highly efficient and possesses a tunable nutrient-releasing characteristic. In the synthesis process, bio-based polyurethane coated urea (BPCU) was prepared by the reaction of bio-polyols with isocyanate. The BPCU was then modified by the layer-by-layer technology to prepare self-assembling modified BPCU (SBPCU). Last, hollow nano-silica (HNS) particles loaded with the sodium alginate (SA) were used to modify SBPCU to fabricate of self-assembling and self-healing BPCU (SSBPCU). The results show that the self-assembled materials were synthesized through electrostatic adsorption. The self-healing was observed through scanning electron microscopy and 3D-X-ray computed tomography, revealing the mechanism was that the repair agent released from HNS reacted with the curing agent to block the pore channels and cracks of the coating. As a result, the SSBPCU exhibited the highest hydrophobicity and surface roughness and thus the slowest release rate. For the first time, this work has designed a novel strategy to solve the bottleneck problem that restricts the development of a controlled-release fertilizer.

Keywords:Model classification Model parameterisation Model ensemble Model structure Rice Uncertainty a b s t r a c t For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.

Nanoscale nutrients are promising for improving crop performance. However, size-induced potential for drifting, segregation, or transformation warrants strategies to streamline fertilization regimes. Herein, we developed three nanofertilizers by coating urea...

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