AgriEngineering

In semi-arid areas, water loss from small agricultural water storage facilities is significant, owing to evaporation. A longitudinal study was conducted between 2019 and 2022 at the Agricultural Research Station, Ananthapuramu, located in the semi-arid climate of Peninsular India, which compared 12 distinct treatments designed to reduce evaporation. These treatments included bamboo sheets, agricultural residues, Azolla (Azolla pinnata), monomolecular alcohol films, and oil-based films, along with an untreated control. Evaporation rates and meteorological data were measured using the depth loss method and automatic weather station. Results indicated substantial treatment effects, such as bamboo sheets decreasing evaporation by 88%, reducing daily loss from 5.2 mm to 0.8 mm, while Azolla achieved a 62% reduction (2.8 mm). Organic residues decreased evaporation by 37–47%, and chemical monolayers and oils by 21–42%. Ridge regression models demonstrated strong performance (R² = 0.789–0.808), with bamboo sheets exhibiting the lowest Root Mean Square Error (0.127 mm day⁻¹). Economic analysis revealed annual water savings of 4700–4800 m³ ha⁻¹ for bamboo sheets and 2300–2500 m³ ha⁻¹ for less effective covers. Assuming a baseline water value of 0.20 US$ m⁻³, annual net benefits ranged from 250 to 900 US$ ha⁻¹, with Net Present Values spanning from 7000 to 160,000 US$ ha⁻¹ across various scenarios. Overall, bamboo sheets and Azolla were identified as the most effective and economically viable options for mitigating evaporation in semi-arid smallholder water systems. Maximum air temperature (T_max) was a key meteorological variable used to model daily evaporation, together with wind speed, followed by relative humidity and sunshine duration.

Soil compaction caused by uncontrolled machinery traffic is a major constraint to sustainable crop production. Controlled Traffic Farming (CTF), which restricts machinery movement to permanent lanes, has been practiced in New Zealand for more than a decade but has not been evaluated against Random Traffic Farming (RTF). This knowledge gap limits farmer awareness and adoption. This study hypothesized that CTF reduces soil compaction and improves soil physical properties compared with RTF. A one-year field experiment was conducted at Pukekohe, New Zealand, using annual ryegrass grown under CTF and RTF. Soil penetration resistance (PR), bulk density, total porosity, moisture content, and air-filled porosity were measured to a 40 cm depth. RTF increased soil PR relative to CTF across 10–40 cm. Bulk density was lower under CTF (0.96–1.03 g·cm⁻³) than RTF (1.11–1.30 g·cm⁻³), with improved total porosity (0.60–0.62 cm·cm⁻³) and aeration (12–23 cm·cm⁻³). CTF achieved a 5.7% higher bed-level yield. When scaled to the whole-field context, the productivity of tramlines contributed to 8% greater dry matter yield under CTF than RTF, indicating that the area allocated to tramlines did not negate the system-level productivity. This study provides the first New Zealand-specific empirical comparison of CTF and RTF to support adoption of CTF.

The growing global population intensifies food demand, challenging the agricultural sector to increase efficiency. Precision agriculture (PA) addresses this challenge by leveraging advanced technologies, such as the Internet of Things (IoT) and sensor networks, to collect and analyze field data. However, accessible tools for storing, managing, and analyzing these data are often limited. This study presents AgDataBox-IoT (ADB-IOT), a novel web application designed to fill this gap by providing a user-friendly platform for optimizing agricultural management. ADB-IOT integrates into the existing AgDataBox ecosystem, extending its capabilities with dedicated IoT functionalities. The application enables farmers to plan IoT networks, visualize and analyze field-collected data through thematic maps and graphs, and monitor and control IoT devices. This integrated approach facilitates informed decision-making, improves control over sustainable soil management, and enhances the overall efficiency of agricultural operations. As a freely accessible tool, ADB-IOT lowers the barrier to adopting precision agriculture technologies.