A comparative experiment was conducted in a large-scale pig farm in Jiangsu Province, China. Emission characteristics of ammonia (NH3), methane (CH4), and nitrous oxide (N2O) were compared between a pig house with robotic daily manure cleaning (RC) and shallow manure pit storage (MS). The experiment was initiated in the spring of 2024 (April 15th) and concluded on June 14th, with a total duration of 61 days. The results showed no difference in ventilation or indoor temperature during the monitoring period, while pig weights ranged from 30 to 81 kg. Under the RC mode, the average daily concentrations of NH3 and CH4 in-barn concentration were 27% and 46% lower than under the MS mode. The corresponding average daily emission factors were 3.4 and 29.9 g d(-1) pig(-1) for NH3 and CH4, representing reductions of 22% and 46% compared to the MS mode. There was no difference in the emission of N2O (p > 0.05). The RC system significantly reduced in-barn concentrations and emission rates of NH3 and CH4, likely by shortening the manure retention time inside the barn. The study showed that robotic manure removal technology shortens manure storage time by increasing the frequency of manure cleaning, enabling the simultaneous reduction of ammonia and methane emissions. It offers a promising technical approach for mitigating gaseous pollution in large-scale pig production.
Drought is the most expensive extreme weather risk to Louisiana agriculture, accounting for 95% of projected crop losses by 2050. Predicted shifts toward more frequent and intense drought will increase the need for supplemental irrigation when aquifer withdrawals already exceed recharge rates. Thus, the objective of this research was to describe and demonstrate the Drought Irrigation Response Tool (DIRT), a freely available, web-based, user-interactive decision support tool for agricultural irrigators that provides site-specific strategies appropriate for concurrent drought risk. DIRT's soil water balance calculation provides a simple representation of volumetric water content (VWC) throughout the crop season for multiple crops, such as corn and soybean production systems. Four on-farm demonstrations were used to observe soil moisture and compare it. The goal of facilitating the determination of the most appropriate day to initiate irrigation events was met in all four datasets, with minimal bias from DIRT. Thus, despite some less-than-ideal accuracies for precise hydrological modeling applications, DIRT is useful in practice for agricultural applications. Further improvements to the model, such as adding considerations for infiltration, refining soil-type selections that affect soil physical properties, and improving various assumptions built into crop growth and weather inputs, must be balanced against the simplicity of the tool to prevent barriers to adoption. Future research should compare DIRT's yield and use efficiency with other irrigation scheduling methods.
Accurate determination of moisture content is essential in tobacco processing, as it directly influences product quality and process stability. Hyperspectral imaging provides a rapid and nondestructive solution for moisture detection; however, the high dimensionality, strong inter-band correlations, and limited interpretability of existing models remain major challenges. To address these issues, this study proposes an improved CNN-LSTM model for predicting moisture content in tobacco materials using hyperspectral data. The proposed approach integrates multiscale CNN networks for local spectral feature extraction with an LSTM network to capture sequential inter-band dependencies. Prior to modeling, spectral data are preprocessed using Savitzky-Golay smoothing and 3 sigma-based outlier filtering to reduce noise and improve data quality. The performance of the proposed model is systematically compared with classical machine learning methods (MLR, PLSR, SVR, and DTR) and conventional deep learning models (RNN and CNN). The results demonstrate that the CNN-LSTM model achieves superior predictive performance, with a test set R2p of 0.9931 and an RMSEP of 0.0037, outperforming all comparative models and existing approaches reported in the literature. To enhance model interpretability, SHAP analysis is employed to quantify wavelength-level contributions, revealing two dominant spectral regions (1180 to 1230 nm and 1350 to 1450 nm), with the latter showing a stronger influence on moisture prediction. This work provides a reliable, interpretable, and visually supported framework for online moisture monitoring in tobacco processing.
Arecanut fruit rot disease, predominantly caused by Phytophthora species, is a major biotic constraint in humid tropical regions, resulting in severe yield losses and economic impact. Accurate early prediction of disease intensity remains challenging due to complex, nonlinear, and delayed interactions between meteorological factors and disease development. This study proposes a lag-aware ensemble learning framework that integrates statistical correlation analysis with machine learning to enhance weather-based forecasting of the intensity of arecanut fruit rot disease. Weekly meteorological variables-including rainfall, field and ambient humidity, wind speed, maximum and minimum air temperature, soil temperature (5 cm and 10 cm depths), and water temperature-were collected from CPCRI meteorological stations, field sensors, and validated farmer observations across multiple disease-prone locations. Disease intensity was quantified using weekly field-observed Percent Disease Intensity (PDI) collected during the monsoon season. Because complete disease observations were unavailable across all monitored locations, missing disease intensity values were estimated using a calibration framework that integrates meteorological variables and a CPCRI-validated, agronomic, rule-based disease-risk score, resulting in a harmonized, full-location PDI dataset used for model development. Pearson and Spearman correlation analyses identified humidity and rainfall as dominant climatic drivers, while lagged Spearman correlation (1-3 weeks) revealed strong delayed effects on disease progression. These statistically validated lagged variables were explicitly incorporated into model development. Multiple models-including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Voting Regressor, and Stacking Regressor were evaluated. Results demonstrate that lag-aware models consistently outperform non-lagged counterparts, with the Random Forest Regressor achieving the highest individual performance (R2 = 0.9545, RMSE = 1.44) and the lag-aware Stacking Regressor attaining comparable accuracy (R2 = 0.9531, RMSE = 1.46). Overall, lag-aware ensemble models improved predictive accuracy by approximately 10% to 15%, confirming their effectiveness as reliable early warning tools for climate-driven disease management in arecanut cultivation.
Highlights Improve the RootBox model and introduce the parameter L-system. Introduction of an angle parameter to control the direction of root growth. Simulate the dynamic change process of root system diameter. Evaluate and analyze the model from different perspectives. Abstract. The rice root is an important organ for absorbing nutrients and water, and its growth directly affects rice development. Because the soil is opaque, obtaining root data is difficult, and observation is blocked. Three-dimensional modeling and visualization can provide an intuitive and effective method for studying rice root growth. In this article, a three-dimensional dynamic model DRoots of rice root system based on the improved RootBox model and parametric L-system is proposed using the rice root system of the YHSM variety as the research object. DRoots was integrated with the morphological features of the rice root system to identify suitable growth functions for both length and diameter. The root system’s growth direction is then constrained by its growth characteristics, and generative rules based on the parametric L-system are proposed and implemented in MATLAB. The DRoots model simulates rice root growth across different growth stages and combines this with its evaluation indices to analyze and evaluate the simulated values of various root system parameters against actual values. The findings suggest that the DRoots model can effectively simulate rice root growth, providing a reference for three-dimensional root modeling in other crops and for the development of smart agriculture. Keywords: CRootBox model, Parametric l-system, Rice root structure, Three-dimensional dynamic modelling.
Automated edible bird's nest (EBN) grading is a critical and challenging task in the agricultural product processing industry, involving the evaluation of complex, interrelated attributes. This study proposes a hierarchical multi-task automated grading framework based on a multi-gate mixture-of-experts (MMoE) architecture. The framework solves the complex EBN grading problem by evaluating multiple, interconnected attributes through shared feature learning. Pre-trained ConvNeXt model extract features from EBN images. The features are subsequently processed by specialized expert networks within the MMoE architecture to enable effective attribute-specific classification for color, shape, impurities, and cracks. MMoE gating mechanisms allow different tasks to adopt their own relevant expert combinations, and multi-task gradient signals can also guide the learning process of backbone features through dynamic weight assignment. Focal loss is used to address class imbalance and improve the model's ability to discriminate. Final EBN grades are determined by a two-stage process that evaluates individual attributes and combines their information to generate five predefined quality grades. Experimental results show that, on a complex, multi-attribute EBN dataset, the proposed method achieves 92% grading accuracy, and all attribute classifications improve. Framework interpretability is demonstrated through performance improvements and Grad-CAM activation maps. This hierarchical, interpretable grading system follows human logic, making decisions transparent so that the reasons for each grade can be traced. The proposed approach is a practical solution to the problem of processing agricultural products.
. Agricultural imaging often requires individual images to be stitched together into a final mosaic for analysis. However, agricultural images can be particularly challenging to stitch because the images contain similar objects, plants are neither rigid nor planar, and mosaics built from many images can accumulate errors that cause drift. Although these issues can be minimized by using images that cover a large area, this precludes the analysis of leaf-scale features. AgRowStitch was created as a user-friendly and open-source pipeline for stitching high-resolution images derived from proximal sensing. The AgRowStitch pipeline operates on single crop rows, so the geometry of the final mosaic can be used to constrain the stitching process. These geometric constraints are used to restrict the image-matching process, filter key point matches generated by SuperPoint and LightGlue, and straighten the mosaics after compositing. AgRowStitch was tested on images taken from three different ground-based imaging platforms. The mosaics produced by AgRowStitch were nearly seamless and had minimal artifacts, while the OpenCV and OpenDroneMap pipelines failed to produce any mosaics of the full region of interest, and Metashape only produced a mosaic of the full imaging area for one dataset. AgRowStitch was robust to small changes in camera orientation and operated without needing additional geospatial information. This makes AgRowStitch a general solution for creating row mosaics suitable for crop monitoring and phenotyping at scales ranging from individual leaves to flowers and fruits.
Highlights Groundwater depletion of the Mississippi River Valley alluvial aquifer threatens agroecosystem sustainability. Direct injection is a high-capacity managed aquifer recharge (MAR) technology to potentially reverse depletion. Leverages excess surface water to support existing irrigation systems with a small infrastructure footprint. MAR implementation example for similar efforts where groundwater depletion impacts farming and water resources. Abstract. Alluvial basins commonly contain both fertile soils and shallow high-yielding aquifers, forming highly productive regions for irrigated agriculture worldwide. The Mississippi Alluvial Plain in the Lower Mississippi River Basin of the United States is one such region, where irrigation demands during the growing season have contributed to long-term depletion of the Mississippi River Valley alluvial aquifer (MRVAA), adversely impacting farming operations in some areas. Managed aquifer recharge (MAR) is a technology for leveraging excess surface water resources by storing water in the subsurface for subsequent beneficial use. A pilot-scale MAR facility that combines riverbank filtration, groundwater transfer, and injection was conceived and built to address sustainability concerns for groundwater irrigation in the Delta region of northwestern Mississippi, USA. This article establishes the need for the facility, describes the steps that led to the decision to pursue it, details the process of securing funding and permissions, and describes the construction and successful shakedown assessments of the facility. During testing of the facility at a flow rate of 94.6 L/s (1,500 gal/min) for 24 h, water level increases of up to 1.48 m (4.85 ft) were measured in the MRVAA at the injection site, whereas water level impacts in the MRVAA at the riverbank filtration extraction site were relatively small, with drawdowns up to 0.91 m (2.99 ft). Pre-operational water sampling results indicate groundwater in the MRVAA at both the extraction and injection sites is anoxic (dissolved oxygen 0.06 to 0.11 mg/L) and elevated iron (1.75 to 31.2 mg/L) and manganese (0.253 to 0.979 mg/L) concentrations indicate reducing geochemical conditions, suggesting chemically compatible conditions for the mixing of recharge water obtained from the extraction well and ambient groundwater at the injection site. Results provide a foundation to potentially expand MAR at a larger scale in the MRVAA in the Delta and an example to pursue similar MAR technology in other intensively cultivated alluvial basins where groundwater level declines are impacting farming and water resources. Keywords: Agroecosystem sustainability, Groundwater injection, Managed aquifer recharge, Mississippi River Valley alluvial aquifer, Riverbank filtration.
Highlights Milled rice quality during postharvest transportation and storage, in Low-leakage enclosure environment, was influenced by prevailing environmental conditions of temperature and relative humidity across different U.S. regions and seasons. Environmental conditions in the winter, particularly for transportation to Michigan when compared to Texas and California, resulted in minimal fissuring, higher mechanical strength, and better moisture retention of milled rice. ANOVA showed cultivar and seasonal effects were highly significant (p < 0.05) for moisture content, fissure percentage, and mechanical strength, highlighting the importance of region- and season-specific transport strategies. Abstract. There is an increasing need to understand the impact of environmental conditions on milled rice quality during postharvest handling and transportation, especially in real industrial scenarios. In this study, the quality responses of three rice cultivars—CLL16 (Long-grain Pureline), XP753 (Long-grain Hybrid), and Titan (Medium-grain Pureline)—were evaluated during transportation in low-leakage enclosures and storage environments across multiple U.S. locations and seasons. The specific objective was to assess how environmental variables such as temperature and relative humidity (RH) during transit influenced the stability of moisture content (MC), fissure formation, and mechanical strength (MS) of milled rice. Rice samples were cleaned, dried to 12.5% moisture content (wet basis), stored at 4°C, then equilibrated to room temperature and milled. Each 500 g milled rice package was dispatched to three locations of interest to the mid-southern rice industry—Texas (TX), Michigan (MN), and California (CA)—during spring, summer, and winter, with three replications per treatment. Initial (pre-transportation) and post-transportation MC, fissure percentage, and MS were recorded to evaluate changes. Winter storage conditions, characterized by lower temperatures and relatively stable RH, were most favorable for preserving milled rice quality. Among the locations, transportation to Michigan resulted in minimal fissure development, higher MS, and moderate MC changes. In contrast, transportation of milled rice in summer to Texas presented the most challenges, with the prevailing high temperature (26.21 ± 2.84°C) and RH (63.69 ± 3.19%) contributing to significant fissuring in the medium-grain rice Titan (49.33 ± 1.15%) and decreased MS in the long-grain hybrid rice XP753 (22.92 ± 0.70 N). Milled rice transported during the Spring displayed moderate fissure development and grain strength loss relative to winter. California, especially during winter and spring, showed the most stable environmental profiles, supporting better moisture retention and structural stability. XP753 showed greater resistance to fissure formation, maintaining the lowest fissure percentages under most conditions, while Titan displayed the highest MS, peaking at 29.14 ± 0.62 N for samples transported to California during spring. Analysis of variance (ANOVA) revealed that cultivar and seasonal effects were highly significant (p < 0.05) for all studied quality attributes, including MC, fissure percentage, and MS, while location-specific factors had notable influence on fissure development and MS. These findings emphasize the need for optimizing milled rice storage and transportation strategies based on seasonal and regional factors, and for selecting resilient cultivars to mitigate quality losses during distribution. Keywords: Environmental conditions, Fissures, Rice breakage, Seasonal effects, Transportation.
. Given the limitations of traditional combine harvesters, such as low efficiency and high emissions, this study developed a distributed electric-drive system for a hybrid combine harvester. The system employs a series hybrid configuration, where an auxiliary power unit (APU) and a power battery supply energy, while independent motors drive key modules, including the header-main shaft, threshing cylinder, cleaning fan, and unloading auger. To improve dynamic performance, a control strategy integrating direct torque control (DTC) with a Luenberger observer-based load torque feedforward compensation was implemented. An adaptive power-allocation strategy enabled the hybrid control unit (HCU) to manage APU operation based on the total power demand and battery state of charge (SOC), thereby maintaining engine operation within the high-efficiency zone. Field tests demonstrated notable performance enhancements: the threshing cylinder limited speed fluctuations to within 3% across varying feeding rates, preventing blockages; the cleaning fan maintained speed stability within 1.5%; and the header-main shaft module exhibited strong overload capacity, with only 0.44% speed variation under impact loads. Travelling speed remained stable at 3.5 to 4.5 km/h. Compared to a conventional harvester, the hybrid prototype achieved a 19.5% reduction in fuel consumption, lower grain breakage and impurity rates, and significantly decreased emissions and noise. This research validates that the proposed distributed electric drive system and control strategies improve operational quality, efficiency, and sustainability, providing a valuable reference for the green and intelligent upgrading of agricultural machinery.
. Accurate evaluation of tomato ripeness and surface quality is essential for intelligent grading and automated sorting in smart agriculture. However, traditional manual inspection methods are often inefficient and inconsistent, limiting their use in large-scale applications. To address these challenges, this article proposed TMSDDet (Tomato Maturity Surface Defect Detection) network, a lightweight detection model based on the YOLO11n framework, specifically designed for tomato maturity classification and defect detection. The model incorporates three optimized modules-ADown (a dual-path downsampling module), Slim-Neck (a lightweight multi-scale fusion structure), and Efficient-Head (an efficient decoupled detection head)-to achieve a strong trade-off between accuracy and computational efficiency. A multi-label tomato image dataset was constructed, including diverse ripeness levels and defect types under varying backgrounds. Experimental results demonstrated that TMSDDet achieved 80.4% mAP@0.5:0.95 with only 1.9M parameters, 3.9 GFLOPs, and an inference time of 6.2 ms per frame (RTX 4060). Compared to mainstream models such as YOLOv6, YOLOv8, and RT-DETR, TMSDDet delivered competitive performance on our test set while maintaining a compact model size of just 4.0 MB. Moreover, the model was successfully deployed on the Jetson Orin Nano without inference acceleration, achieving stable real-time performance (inference time: 31.0 ms). These results indicated that TMSDDet was an efficient, robust, and deployable solution for tomato freshness and defect detection, offering strong potential for practical deployment in resource-constrained agricultural environments.
Agricultural engineers conducting research or practicing in industry can deploy a large variety of instruments to investigate, monitor, and control agricultural practices. Locating instruments for optimal performance in agricultural fields and facilities can be challenging and expensive. There is a need for an economical platform to monitor agricultural crops across plots and fields. The goal of the current endeavor was to develop an economical, elevated camera-and-sensor platform that could be constructed from common building materials with readily available shop tools. The designed camera mast positioned cameras 4 m above the ground and had two arms, each extending 3.8 m horizontally at the top of the mast, placing cameras over crops. Cameras were mounted at the ends of the mast arms on French cleats for ease of installation and retrieval from the ground using a lifting/extension pole. The load capacity inherent in the design made it possible to add sensors and instruments to the mast arms. The camera mast successfully supported two time-lapse cameras that recorded color images of peanut plant growth during the 2025 growing season.
The uneven distribution of temperature and humidity in intensive curing barns is a key factor affecting the consistency of cured tobacco leaf quality. To enhance the quality of tobacco leaves after curing, this study is based on the ten-steady-temperature-point curing process, adopts an air downdraft-type bulk curing barn, installs 55 temperature and humidity measuring points, and collects data every 10 minutes. Firstly, the temperature and humidity distributions within the curing barn were analyzed from both temporal and spatial perspectives. Secondly, the degree of temperature deviation was quantified and correlated with the distribution of quality in cured tobacco leaves. Finally, a temperature equalization device was designed, and its effectiveness was evaluated. The results indicate that: (1) By analyzing, it is possible to accurately divide the interior of the curing barn into three categories: high temperature and low humidity zone, moderate temperature and medium humidity zone, and low temperature and high humidity zone, and the proportion of poorly cured tobacco leaves was 10.3%, 9.3%, and 13.9%, respectively. (2) The temperature equalization device can improve the uniformity of temperature and humidity distribution within the curing barn to a certain extent, reducing the mean coefficient of variation (C.V.) of temperature by 0.83, the mean C.V. of relative humidity by 2.52, and the proportion ofpoorly cured tobacco leaves by 3.7%. Compared with the original curing barn, there was a reduction of 18.62 kg ofpoorly cured tobacco leaves per curing cycle in the improved barn. This study provides a theoretical basis for adjusting the tobacco curing process and optimizing the curing device.
In agriculture, weeds compete with crops for essential resources, often reducing crop yields. Accurate and efficient discrimination between crops and weeds is therefore essential for improving agricultural productivity and enabling effective weed management. Deep learning-based segmentation models such as U-Net and YOLOv11 have shown promise for weed classification tasks, but they differ in terms of segmentation accuracy and computational efficiency. In this study, we used pixel-level masks generated by the Segment Anything Model (SAM) to train both U-Net and YOLOv11, and conducted a comparative evaluation of their performance in weed segmentation. The models were assessed for segmentation accuracy and inference time. The results offered insights into the trade-offs between these approaches and provided guidance for selecting suitable models for real-time weed segmentation in agricultural applications. Compared with YOLOv11, U-Net achieves higher segmentation accuracy of up to 3.26%, and a lighter U-Net model offers faster inference times of up to 1.76 & times;, making it suitable for real-time tasks.
. The rice root is an important organ for absorbing nutrients and water, and its growth directly affects rice development. Because the soil is opaque, obtaining root data is difficult, and observation is blocked. Three-dimensional modeling and visualization can provide an intuitive and effective method for studying rice root growth. In this article, a three-dimensional dynamic model DRoots of rice root system based on the improved RootBox model and parametric L-system is proposed using the rice root system of the YHSM variety as the research object. DRoots was integrated with the morphological features of the rice root system to identify suitable growth functions for both length and diameter. The root system's growth direction is then constrained by its growth characteristics, and generative rules based on the parametric L-system are proposed and implemented in MATLAB. The DRoots model simulates rice root growth across different growth stages and combines this with its evaluation indices to analyze and evaluate the simulated values of various root system parameters against actual values. The findings suggest that the DRoots model can effectively simulate rice root growth, providing a reference for three-dimensional root modeling in other crops and for the development of smart agriculture.
A working group composed of individuals from industry and universities was formed over four years ago and prepared a new standard (ASABE S658) for evaluating planter seed spacing and monitoring system performance. This voluntary test standard is composed of three parts: center dot S658-1, Singulating Seeding Equipment Test Methods Part1: General Information (ASABE Standards, 2024a) center dot S658-2, Singulating Seeding Equipment Test Methods Part 2: Monitoring Systems Performance (ASABE Standards, 2024b) center dot S658-3, Singulating Seeding Equipment TestMethods Part 3: Seed Spacing Performance (ASABE Standards, 2024c) The X658 working group as part of the ASABE MS-49 Crop Production Systems, Machinery, and Logistics Committee believes there is a need to communicate and introduce the details and rationale of these test procedures to the planter related industry and the ASABE community so that going forward these procedures can be used when comparing the performance of these systems and thus minimizing any ambiguity when communicating performance. This article focuses on testing planter monitoring systems (Parts 1 and 2), while testing row unit performance (Parts 1 and 3) is described in Kocher et al. (2025). In addition to the nuts and bolts of the testing procedure and analysis in the Standard document, ASABE hosts Excel workbooks that include the necessary programming so that, when test results are entered, performance measures are calculated, graphs are plotted, and results are automatically entered into the report formats. This article also includes a discussion of concerns the working group had during the development of the Standard.
Accurate counting of soybean pods and seeds is essential for yield prediction, crop management, and variety improvement. However, existing automatic methods under controlled indoor conditions often exhibit limited computational efficiency, insufficient accuracy, and limited practical deployment for reducing manual workload. To address this, we propose an Indoor Soybean Counting Framework (ISCF), a lightweight deep learning framework that decouples localization and classification into two independent stages to count soybean pods and seeds. ISCF first performs precise segmentation of soybean pods using the proposed Indoor Soybean Segmentation Network (ISSN), followed by classification of the number of seeds per pod using a MobileNetV3-based architecture. Optimized for lightweight design, ISCF is well-suited to realtime deployment on edge devices. Experimental results demonstrate the superior performance ofISCF in soybean pod and seed counting tasks, achieving an AP50 of 99.5% for pod segmentation, a mean absolute error (MAE) of merely 0.72, and an R2 of 0.9942 for pod counting, and an MAE of 3.79 and an R2 of 0.9573 for seed counting. Moreover, ISCF generalizes well to datasets from four additional crop species, underscoring its potential for a broad range of indoor crop counting and phenotyping applications.
The use of unmanned aerial systems (UAS) for seeding cover crops is increasing rapidly, whereas limited information exists on their application performance. Therefore, studies were conducted to assess the application rate (metering) accuracy and distribution uniformity of cover crop seed (cereal rye) applied with a UAS (DJI Agras T25). Static tests were conducted to determine the actual material flow rate from the hopper at different metering (hopper) gate openings. Field tests were conducted to determine the applied rate and to assess the uniformity of cereal rye distribution across the swath at different target rates and flight speeds. The actual flow rate (kg min-1) differed from the values suggested by the internal calibration, resulting in a significant underapplication of cereal rye (9.5%-17.7%) across different target rates and flight speeds during the field tests. The single-pass spread patterns showed leftward skewness, indicating greater material distribution (51% to 64%) towards the left and less material deposited (29% to 40%) towards the right. The simulated overlap spread pattern analysis indicated no effect of application rate or flight speed on the distribution uniformity of cereal rye. However, the one-direction application method exhibited improved material deposition within the swath compared to the progressive method. A CV analysis at different simulated effective swaths indicated no considerable improvement in the distribution uniformity at narrower operating swaths. Future research should investigate the effect of other operational parameters, such as application height and spinner-disc speed, on effective swath and uniformity, along with testing different cover crop seeds (mixtures).
Broiler producers must have access to sufficient water supplies to maintain the health and well-being ofbroilers (meat-type chickens). Currently, the only water sources available to broiler producers are well water and municipal water. In areas of broiler production that rely on well water, producers can experience issues with low-yield or poor water quality, which can cause damage to equipment and become expensive to treat. Producers who have access to municipal water can have a more reliable water source; however, in some areas of the U.S., water rates have increased. This has been the experience for broiler producers in Cullman, Alabama, where the county water department announced customers would see increased water rates beginning in 2015. In anticipation of increased rates, a rainwater harvesting (RWH) system was constructed in 2016 on a four-house commercial broiler farm in Cullman to help offset rising water costs. While the producer has observed a reduction in their monthly water bill, the system did not include an effective way of measuring rainwater use (RWU) and municipal water use (MWU). Therefore, the goal of this study was to provide a description of the RWH system and to evaluate system performance by monitoring daily MWU and RWU over a 12-month period in 2024. Daily MWU and RWU were monitored across six consecutive flocks from 12 January 2024 to 3 January 2025 using wireless ultrasonic water meters. Total water use (TWU) during production was 7,267,625 L, where MWU represented 45% (3,250,570 L) and RWU represented 55% (4,017,056 L) of TWU, with an estimated cost savings of $17,017 over the study period. Producer feedback on the system operation and performance has been crucial in understanding where design changes could be made to improve the system and how important regular monitoring and maintenance was to achieve system performance.
There is an increasing need to understand the impact of environmental conditions on milled rice quality during postharvest handling and transportation, especially in real industrial scenarios. In this study, the quality responses of three rice cultivars-CLL16 (Long-grain Pureline), XP753 (Long-grain Hybrid), and Titan (Medium-grain Pureline)-were evaluated during transportation in low-leakage enclosures and storage environments across multiple U.S. locations and seasons. The specific objective was to assess how environmental variables such as temperature and relative humidity (RH) during transit influenced the stability of moisture content (MC), fissure formation, and mechanical strength (MS) of milled rice. Rice samples were cleaned, dried to 12.5% moisture content (wet basis), stored at 4 degrees C, then equilibrated to room temperature and milled. Each 500 g milled rice package was dispatched to three locations of interest to the mid-southern rice industry-Texas (TX), Michigan (MN), and California (CA)-during spring, summer, and winter, with three replications per treatment. Initial (pre-transportation) and post-transportation MC, fissure percentage, and MS were recorded to evaluate changes. Winter storage conditions, characterized by lower temperatures and relatively stable RH, were most favorable for preserving milled rice quality. Among the locations, transportation to Michigan resulted in minimal fissure development, higher MS, and moderate MC changes. In contrast, transportation of milled rice in summer to Texas presented the most challenges, with the prevailing high temperature (26.21 + 2.84 degrees C) and RH (63.69 + 3.19%) contributing to significant fissuring in the medium-grain rice Titan (49.33 + 1.15%) and decreased MS in the long-grain hybrid rice XP753 (22.92 + 0.70 N). Milled rice transported during the Spring displayed moderate fissure development and grain strength loss relative to winter. California, especially during winter and spring, showed the most stable environmental profiles, supporting better moisture retention and structural stability. XP753 showed greater resistance to fissure formation, maintaining the lowest fissure percentages under most conditions, while Titan displayed the highest MS, peaking at 29.14 + 0.62 N for samples transported to California during spring. Analysis of variance (ANOVA) revealed that cultivar and seasonal effects were highly significant (p < 0.05) for all studied quality attributes, including MC, fissure percentage, and MS, while location-specific factors had notable influence on fissure development and MS. These findings emphasize the need for optimizing milled rice storage and transportation strategies based on seasonal and regional factors, and for selecting resilient cultivars to mitigate quality losses during distribution.
