Purpose: Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas. Materials and methods: In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data. Results and discussion: Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R²_adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R² = 0.99), and the SVM model was the lowest (R² = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model. Conclusion: The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy.

Climate change threatens long‐term soil health because of increased severity and frequency of drought periods. Applying biochar to soils before a drought can increase non‐biochar soil carbon (C) and water storage over the long term and sustain crop yield. However, the on‐farm benefit of buried solid biochar and applied liquid biochar at low rates remains uncertain. This study examined the effects of two novel biochar‐based soil amendments on soil C, water storage and crop yield. The biochar‐based amendments included a biochar reactive barrier (RB) made by layering wood‐based biochar, straw mulch and cow manure into a series of open surface trenches, and a liquid biochar mineral complex (BMC) applied twice, at low rate (200 kg ha⁻¹) to one side of RB (fertilised area), while the other side of RB received no treatments (non‐fertilised area). Moisture concentration within the RB ranged from 6.76% up to 56.68% after large rainfall, more than double the surrounding soils and gradually started migrating from the RB outwards. Soil within 50 cm distance of the RB showed a 24.5% increase in non‐biochar soil C compared with soil at 600 cm distance of the RB, 2.54% versus 2.04%, respectively, in the non‐fertilised area, which was supported with lowering soil microbial activity. Pasture yield increase was associated with liquid BMC fertiliser rather than proximity to the RB. Pasture yield was 44% higher in the fertilised area compared with the non‐fertilised area 27.89 t ha⁻¹ versus 19.31 t ha⁻¹. Approximately 158 kg CO₂e was removed from the atmosphere for each cubic meter of RB and an annual removal of 150 kg CO₂e ha⁻¹ was estimated by liquid BMC application. Income earned by increased yield was still profitable even though applied liquid BMC could cost between USD 400–520 ha⁻¹ including shipping costs. Overall, our study suggested biochar‐based RB and BMC fertilisers can effectively increase soil moisture retention while building non‐biochar soil C storage in the surrounding soil. The adoption of biochar‐based techniques has the potential to improve drought resilience while increasing soil C in wide range of non‐irrigated cropping systems.

Purpose: Revegetation of riparian zones is important to improve their soil nitrogen (N) dynamics and to preserve their microbial compositions. However, the success of revegetation projects currently depends on weed control to reduce non-target vegetation competing over nutrients and to ensure the target plant species growth and survival. Different weed control methods affect soil microbial composition and N cycling. However, the long-term effects of herbicides on soil nitrogen (N) pools and microbial community composition remain uncertain even after cessation of the herbicide application. Materials and methods: This study compared the impacts of different herbicides (Roundup^®, BioWeed™, Slasher^®, and acetic acid) with mulch on soil N dynamics and microbial community structure 3 years after vegetation establishment (herbicides applied repeatedly in the first 2 years after which no herbicides were applied in the third final year). Results and discussion: Soil microbial biomass carbon (MBC) was significantly higher in mulch compared with Roundup^®, BioWeed™, Slasher^®, and acetic acid at month 26 at the Kandanga site and month 10 at the Pinbarren site. Soil MBC remained significantly higher in mulch compared with Roundup^® and BioWeed™, 12 months after the cessation of herbicide application at the Pinbarren site. Soil MBC in the Roundup^® and BioWeed™ groups was also lower than the acceptable threshold (160 mg kg⁻¹) at month 34 at the Pinbarren site. Soil NO₃⁻-N was significantly higher in the mulch than the Roundup^® at months 22 and 34 after revegetation at the Pinbarren site which could be partly explained by the decreased abundance of the denitrifying bacteria (Candidatus solibacter and C. koribacter). Additionally, both soil bacterial and fungal communities at the Pinbarren site and only fungal community at the Kandanga site were different in the mulch group compared with all other herbicides. The differences persisted 12 months after the cessation of herbicide application at the Pinbarren site. Conclusion: Our study suggested that the application of mulch to assist with riparian revegetation would be beneficial for soil microbial functionality. The use of herbicides may have long-lasting effects on soil microbial biomass and diversity and therefore herbicides should be used with caution as part of an integrated land management plan.

Tree nuts are rich in nutrients, and global production and consumption have doubled during the last decade. However, nuts have a range of quality defects that must be detected and removed during post-harvest processing. Tree nuts can develop hidden internal discoloration, and current sorting methods are prone to subjectivity and human error. Therefore, non-destructive, real-time methods to evaluate internal nut quality are needed. This study explored the potential for VNIR (400–1000 nm) hyperspectral imaging to classify brown center disorder in macadamias. This study compared the accuracy of classifiers developed using images of kernels imaged in face-up and face-down orientations. Classification accuracy was excellent using face-up (>97.9%) and face-down (>94%) images using ensemble and linear discriminate models before and after wavelength selection. Combining images to form a pooled dataset also provided high accuracy (>90%) using artificial neural network and support vector machine models. Overall, HSI has great potential for commercial application in nut processing to detect internal brown centers using images of the outside kernel surface in the VNIR range. This technology will allow rapid and non-destructive evaluation of intact nut products that can then be marketed as a high-quality, defect-free product, compared with traditional methods that rely heavily on representative sub-sampling.

Sustaining crop yield is a growing challenge due to soil fertility loss and climate change. As a result, fertiliser application is increasing to sustain yield and counteract low fertiliser use efficiency. Developing nutrient-efficient fertilisers, for example by combining biochar with fertilisers, is thus crucially needed. In this context, the development of biochar-mineral complex (BMC) fertilisers into liquefied products suitable for broadacre application is highly innovative. This study aimed to explore the agronomic benefits and economic viability of four novel liquefied BMCs designed for application within a pasture cropping system. The different forms of developed BMCs included two non-enriched BMCs (BMC1 and BMC2) that were micronised using different techniques, a phosphorus (P)-enriched BMC (BMC3), and a nitrogen (N)-enriched BMC (BMC4), all applied both with- and without co-fertilisation. Soil biochemistry, plant NP uptake, NP balances, plant yield and cost–benefit were examined. Pasture yield was significantly higher using BMC1 and BMC3 with co-fertilisation than without, most likely due to improved P availability, as evidenced by positive P balances. Yield was significantly enhanced under BMC4 at 42.20 t ha ⁻¹ , compared with BMC1 at 21.90 t ha ⁻¹ , BMC2 at 19.41 t ha ⁻¹ , BMC3 at 25.81 t ha ⁻¹ , the fertilised control at 18.75 t ha ⁻¹ and non-fertilised control at 11.53 t ha ⁻¹ . Only BMC4 showed positive NP balances, suggesting that pasture did not absorb N and P from the existing soil reserve. Soil microbial composition and abundance were not affected by the applied BMC treatments in short term. The benefit–cost ratio ranged from 1.94 to 2.54, indicating economic viability of the BMC products and suggesting a great potential of biochar-based fertilisers for wide adoption to other crops. Graphic abstract