Current status of community resources and priorities for weed genomics research.

Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

Laser weeding: opportunities and challenges for couch grass (Elymus repens (L.) Gould) control.

Laser weeding may contribute to less dependency on herbicides and soil tillage. Several research and commercial projects are underway to develop robots equipped with lasers to control weeds. Artificial intelligence can be used to locate and identify weed plants, and mirrors can be used to direct a laser beam towards the target to kill it with heat. Unlike chemical and mechanical weed control, laser weeding only exposes a tiny part of the field for treatment. Laser weeding leaves behind only ashes from the burned plants and does not disturb the soil. Therefore, it is an eco-friendly method to control weed seedlings. However, perennial weeds regrow from the belowground parts after the laser destroys the aerial shoots. Depletion of the belowground parts for resources might be possible if the laser continuously kills new shoots, but it may require many laser treatments. We studied how laser could be used to destroy the widespread and aggressive perennial weed Elymus repens after the rhizomes were cut into fragments. Plants were killed with even small dosages of laser energy and stopped regrowing. Generally, the highest efficacy was achieved when the plants from small rhizomes were treated at the 3-leaf stage.

Ecological weed management and square planting influenced the weed management, and crop productivity in direct-seeded rice.

Herbicide use may pose a risk of environmental pollution or evolution of resistant weeds. As a result, an experiment was carried out to assess the influence of different non-chemical weed management tactics (one hoeing (HH) at 12 DAS followed by (fb) one hand weeding at 30 DAS, one HH at 12 DAS fb Sesbania co-culture and its mulching, one HH at 12 DAS fb rice straw mulching @ 4t ha-1, one HH at 12 DAS fb rice straw mulching @ 6 t ha-1) on weed control, crop growth and yield, and economic returns in direct-seeded rice (DSR). Experiment was conducted during kharif season in a split-plot design and replicated thrice. Zero-till seed drill-sown crop (PN) had the lowest weed density at 25 days after sowing (DAS), while square planting geometry (PS) had the lowest weed density at 60 DAS. PS also resulted in a lower weed management index (WMI), agronomic management index (AMI), and integrated weed management index (IWMI), as well as higher growth attributes, grain yield (4.19 t ha-1), and net return (620.98 US$ ha-1). The cultivar Arize 6444 significantly reduced weed density and recorded higher growth attributes, yield, and economic return. In the case of weed management treatments, one HH at 12 DAS fb Sesbania co-culture and its mulching had the lowest weed density, Shannon-weinner index and eveness at 25 DAS. However, one hoeing at 12 DAS fb one hand weeding at 30 DAS (HH + WH) achieved the highest grain yield (4.85 t ha-1) and net returns (851.03 US$ ha-1) as well as the lowest weed density at 60 DAS. PS × HH + WH treatment combination had the lowest weed persistent index (WPI), WMI, AMI, and IWMI, and the highest growth attributes, production efficiency, and economic return.

Optimizing herbicide selection for pre-emergence control of itchgrass and cypressvine morningglory in sugarcane.

The aim of this study was to assess the efficacy of herbicides in association to control Rottboellia exaltata and Ipomoea quamoclit during pre-emergence while also to evaluate the potential impact on the sugarcane. The experimental design employed a randomized block with seven treatments and four replications. The treatments were: 1 - no herbicide application; 2 - indaziflam + sulfentrazone; 3 - indaziflam + diclosulam; 4 - indaziflam + tebuthiuron; 5 - flumioxazin + diclosulam, 6 - flumioxazin + pyroxasulfone and 7 - clomazone + sulfentrazone. The evaluated parameters were: percentage of weeds control, green coverage percentage (Canopeo® system), weed biomass (g m-2), itchgrass height, and sugarcane tiller. Several herbicide associations have been proven effective alternatives for managing itchgrass and cypressvine morningglory. The most successful treatments for itchgrass control were indaziflam + tebuthiuron (100%) and indaziflam + diclosulam (97%), whereas for cypressvine morningglory, the betters were indaziflam + sulfentrazone (97%), indaziflam + diclosulam (98%), indaziflam + tebuthiuron (97%), flumioxazin + diclosulam (94%), and clomazone + sulfentrazone (96%). All treatments reduced the weed biomass, with indaziflam + tebuthiuron being the safest option for protecting sugarcane.

Trade-offs constrain the success of glyphosate-free farming.

Glyphosate, the most widely used herbicide, is linked with environmental harm and there is a drive to replace it in agricultural systems. We model the impacts of discontinuing glyphosate use and replacing it with cultural control methods. We simulate winter wheat arable systems reliant on glyphosate and typical in northwest Europe. Removing glyphosate was projected to increase weed abundance, herbicide risk to the environment, and arable plant diversity and decrease food production. Weed communities with evolved resistance to non-glyphosate herbicides were not projected to be disproportionately affected by removing glyphosate, despite the lack of alternative herbicidal control options. Crop rotations with more spring cereals or grass leys for weed control increased arable plant diversity. Stale seedbed techniques such as delayed drilling and choosing ploughing instead of minimum tillage had varying effects on weed abundance, food production, and profitability. Ploughing was the most effective alternative to glyphosate for long-term weed control while maintaining production and profit. Our findings emphasize the need for careful consideration of trade-offs arising in scenarios where glyphosate is removed. Integrated Weed Management (IWM) with more use of cultural control methods offers the potential to reduce chemical use but is sensitive to seasonal variability and can incur negative environmental and economic impacts.

Understanding the Effects of Ligand Configuration on Protoporphyrinogen IX Oxidase with Rationally Designed 3-(N-Phenyluracil)but-2-enoates.

Protoporphyrinogen IX oxidase (PPO, EC 1.3.3.4) is a promising target for green herbicide discovery. However, the ligand configuration effects on PPO activity were still poorly understood. Herein, we designed 3-(N-phenyluracil)but-2-enoates using our previously developed active fragments exchange and link (AFEL) approach and synthesized a series of novel compounds with nanomolar ranges of Nicotiana tabacum PPO (NtPPO) inhibitory potency and promising herbicidal potency. Our systematic structure-activity relationship investigations showed that the E isomers of 3-(N-phenyluracil)but-2-enoates displayed improved bioactivity than their corresponding Z isomers. Using molecular simulation studies, we found that the E isomers showed a relatively lower entropy change and could sample more stable binding conformation to the receptor than the Z isomers. Our density functional theory (DFT) calculations showed that the E isomers showed higher chemical reactivity and lower electronic chemical potential than their corresponding Z isomers. Compound E-Ic emerged as the optimal compound with a Ki value of 3.0 nM against NtPPO, exhibiting a broader spectrum of weed control than saflufenacil at 37.5-75 g ai/ha and also safe to maize at 75 g ai/ha, which could be considered as a promising lead herbicide for further development.

Isolation and chemical characterization of lignocellulosic fiber from Pueraria montana using Box-Behnken design for weed management.

The huge demand for natural fibers necessitates the search for non-traditional bioresources including invasive species which are deteriorating the ecosystem and biodiversity. The study aims to utilize Pueraria montana weed for the extraction of lignocellulosic fiber using both traditional (water retting) and chemical extraction methods to determine the better extraction method. Chemically extracted fiber showed 17.09 g/tex bundle strength whereas water-extracted fiber showed 11.7 g/tex bundle strength. Therefore, chemical extraction method was chosen for fiber isolation by optimization of reaction conditions using Box Behnken Design. Based on the design, optimal conditions obtained were 1 % w/v NaOH, 0.75 % v/v H2O2, and 3 days retting time. Solid-state NMR illustrated the breakdown of hemicellulose linkages at 25.89 ppm. FTIR revealed the disappearance of C=O groups of hemicellulose at 1742 cm-1. TGA demonstrated thermal stability of chemically treated fiber up to 220 °C and activation energy of 60.122 KJ/mol. XRD evidenced that chemically extracted fiber has a crystallinity index of 71.1 % and a crystal size of 2 nm. Thus P. montana weed holds potential for the isolation of natural fiber as its chemical composition and properties are comparable to commercial lignocellulosic fibers. The study exemplifies the transformation of weed to a bioresource of natural fibers.

New Perspective on the Use of α-Bisabolol for Weed Control.

The indiscriminate use of synthetic herbicides reduces its effectiveness. Bioherbicides produced with metabolites emerge as an alternative to managing weeds. We aimed to analyze the phytotoxic potential of the essential oil of Vanillosmopsis arborea (EOVA) and the α-bisabolol molecule, its main component. We evaluated the effects of EOVA and α-bisabolol at different concentrations on the germination, growth, antioxidant metabolism, and photosynthesis of different species. EOVA and α-bisabolol showed promising phytotoxic effects on the germination and initial growth of the weed Senna occidentalis, inhibiting the activity of the antioxidant enzymes and increasing lipid peroxidation. α-Bisabolol reduced the weed seedling growth by inducing oxidative stress, which suggests a greater role in postemergence. Moreover, in the weed postemergence, both EOVA and α-bisabolol caused damage in the shoots, reduced the chlorophyll content, and increased lipid peroxidation besides reducing photosynthesis in S. occidentalis. Overall, we suggest the promising action of α-bisabolol and EOVA as bioherbicides for weed control.

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks.

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image-classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf. RESULTS: Weed-detection NNs, including DenseNet, GoogLeNet and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k-fold cross-validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing data sets for estimating weed coverage. The inference speed of ResNet was similar to that of GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network for weed detection and coverage estimation. CONCLUSION: These results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing calculation of the herbicide requirements for variable-rate herbicide applications. The proposed method can be employed in a machine vision-based autonomous site-specific spraying system of smart sprayers. © 2024 Society of Chemical Industry.

Stereo Vision for Plant Detection in Dense Scenes.

Automated precision weed control requires visual methods to discriminate between crops and weeds. State-of-the-art plant detection methods fail to reliably detect weeds, especially in dense and occluded scenes. In the past, using hand-crafted detection models, both color (RGB) and depth (D) data were used for plant detection in dense scenes. Remarkably, the combination of color and depth data is not widely used in current deep learning-based vision systems in agriculture. Therefore, we collected an RGB-D dataset using a stereo vision camera. The dataset contains sugar beet crops in multiple growth stages with a varying weed densities. This dataset was made publicly available and was used to evaluate two novel plant detection models, the D-model, using the depth data as the input, and the CD-model, using both the color and depth data as inputs. For ease of use, for existing 2D deep learning architectures, the depth data were transformed into a 2D image using color encoding. As a reference model, the C-model, which uses only color data as the input, was included. The limited availability of suitable training data for depth images demands the use of data augmentation and transfer learning. Using our three detection models, we studied the effectiveness of data augmentation and transfer learning for depth data transformed to 2D images. It was found that geometric data augmentation and transfer learning were equally effective for both the reference model and the novel models using the depth data. This demonstrates that combining color-encoded depth data with geometric data augmentation and transfer learning can improve the RGB-D detection model. However, when testing our detection models on the use case of volunteer potato detection in sugar beet farming, it was found that the addition of depth data did not improve plant detection at high vegetation densities.

Acting pre-emptively reduces the long-term costs of managing herbicide resistance.

Globally, pesticides improve crop yields but at great environmental cost, and their overuse has caused resistance. This incurs large financial and production losses but, despite this, very diversified farm management that might delay or prevent resistance is uncommon in intensive farming. We asked farmers to design more diversified cropping strategies aimed at controlling herbicide resistance, and estimated resulting weed densities, profits, and yields compared to prevailing practice. Where resistance is low, it is financially viable to diversify pre-emptively; however, once resistance is high, there are financial and production disincentives to adopting diverse rotations. It is therefore as important to manage resistance before it becomes widespread as it is to control it once present. The diverse rotations targeting high resistance used increased herbicide application frequency and volume, contributing to these rotations' lack of financial viability, and raising concerns about glyphosate resistance. Governments should encourage adoption of diverse rotations in areas without resistance. Where resistance is present, governments may wish to incentivise crop diversification despite the drop in wheat production as it is likely to bring environmental co-benefits. Our research suggests we need long-term, proactive, food security planning and more integrated policy-making across farming, environment, and health arenas.

Ambrosia grayi as a new alien causal species in Israel: plant biology and chemical management.

BACKGROUND: Ambrosia grayi is a perennial weed native to northern Mexico, which can also be found in the Great Plains of the US. Outside the Americas, A. grayi has only been documented in Israel, where it is currently categorized as a casual species at advanced eradication stages. Here, we studied the plant biology and chemical weed management options of A. grayi. RESULTS: Only large achenes of A. grayi (~5% of all achenes) contain seeds; moreover, the viability of seeds extracted from large achenes was ~25%. Examination of plant anatomy revealed that underground vegetative segments show an anatomical structure of stems (rhizomes) with anomalous secondary growth. The optimal (night/day) temperature for the emergence of A. grayi rhizomes was 20/30 °C, and the emergence rate increased under elevated temperatures. Emergence may occur at different soil moisture content (25-60%); rhizomes were able to emerge even after 1 month of drought conditions (20%, 25% and 30%). Herbicide combinations, such as fluroxypyr + glufosinate, fluroxypyr + glyphosate, and glyphosate + saflufenacil + surfactant, were tested under quarantine conditions and showed high efficacy for the control of A. grayi. However, the efficiency of these treatments was highly correlated with plant growth stage. CONCLUSION: In Israel, the spread of A. grayi occurs mainly via rhizomes that can emerge under a wide range of temperatures and soil moisture conditions. Data regarding herbicide efficacy will aid in improving the eradication efforts taken by Israel's Plant Protection and Inspection Services. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Early silique-shedding wild radish (Raphanus raphanistrum L.) phenotypes persist in a long-term harvest weed seed control managed field in Western Australia.

BACKGROUND: This study introduces a wild radish population collected from Yelbeni in the Western Australian grainbelt that evolved an early silique abscission (shedding) trait to persist despite long-term harvest weed seed control (HWSC) use. In 2017, field-collected seed (known herein as Yelbeni) was compared to surrounding ruderal and field-collected populations in a fully randomized common garden study. RESULTS: The Yelbeni population exhibited a higher rate of silique abscission when compared to the ruderal populations collected from the site before wheat (Triticum aestivum L.) harvest (assessed at soft dough stage, Zadoks 83). A similar common garden study was conducted in the subsequent season (2018) using progeny reproduced on a single site without stress. The HWSC-selected progeny (Yelbeni P) shed 1048 (±288) siliques before wheat maturity at the soft dough stage (Zadoks 83) compared to 25 (±7) siliques from the pooled control populations. The Yelbeni P population only flowered 6 days earlier (FT50 as determined by log-logistic analysis) than pooled control populations, which is unlikely to fully account for the increased rate of silique abscission. The Yelbeni P population also located its lowest siliques below the lowest height for harvest interception (10 cm), which is likely to increase HWSC evasion. The mechanism inducing early silique-shedding is yet to be determined; however, wild radish is known for its significant genetic variability and has demonstrated its capacity to adapt to environmental and management stresses. CONCLUSION: This study demonstrates that the repeated use of HWSC can lead to the selection of HWSC-avoidance traits including early silique-shedding before harvest and/or locating siliques below the harvest cutting height for interception. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa.

BACKGROUND: Accurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy for broadleaf weeds growing in alfalfa. RESULTS: A novel neural network, ResNet-101-segmentation, was developed by fusing an image classification and segmentation module with the backbone selected from ResNet-101. Compared with existing neural networks (AlexNet, GoogLeNet, VGG16, and ResNet-101), ResNet-101-segmentation improved the detection of Carolina geranium, catchweed bedstraw, mugwort and speedwell from 78.27% to 98.17%, from 79.49% to 98.28%, from 67.03% to 96.23%, and from 75.95% to 98.06%, respectively. The novel network exhibited high values of confusion matrices (>90%) when trained with sufficient data sets. CONCLUSION: ResNet-101-segmentation demonstrated excellent performance compared with existing models (AlexNet, GoogLeNet, VGG16, and ResNet-101) for detecting broadleaf weeds growing in alfalfa. This approach offers a promising solution to increase the accuracy of weed detection, especially in cases where weeds and crops have similar plant morphology. © 2024 Society of Chemical Industry.

Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage.

BACKGROUND: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds. RESULTS: The precision, recall, F1-score, mAP0.5, mAP0.5:0.95 of You Only Look Once (YOLO) v5 for detecting cabbage were 0.986, 0.979, 0.982, 0.995, and 0.851, respectively, while these metrics were 0.973, 0.985, 0.979, 0.993, and 0.906 for YOLOv8, respectively. However, none of these metrics exceeded 0.891 when detecting weeds. The reduced performances for directly detecting weeds could be attributed to the diverse weed species at varying densities and growth stages with different plant morphologies. A segmentation procedure demonstrated its effectiveness for extracting weeds outside the bounding boxes covering the crops, and thereby realizing effective indirect weed detection. CONCLUSION: The indirect weed detection approach demands less manpower as the need for constructing a large training dataset containing a variety of weed species is unnecessary. However, in a certain case, weeds are likely to remain undetected due to their growth in close proximity with crops and being situated within the predicted bounding boxes that encompass the crops. The models generated in this research can be used in conjunction with the machine vision subsystem of a smart sprayer or mechanical weeder. © 2024 Society of Chemical Industry.

Non-destructive environmental safety assessment of threatened and endangered plants in weed biological control.

Assessing the risk of nontarget attack (NTA) for federally listed threatened and endangered (T&E) plant species confamilial to invasive plants targeted for classical biological control, is one of the most important objectives of pre-release environmental safety assessments in the United States. However, evaluating potential NTA on T&E species is often complicated by restrictive agency requirements for obtaining propagules, or the ability to propagate plants and rear agents to the appropriate phenostages synchronously for testing, or both. Here, we assessed whether plant cues associated with a host recognition can be used for testing the attractiveness of four T&E and one rare single population plant species non-destructively for a candidate biocontrol agent. We used the seed-feeding weevil, Mogulones borraginis, a candidate for the biological control of the invasive plant, Cynoglossum officinale (Boraginaceae) as the study system. We collected olfactory and visual cues in the form of flowering sprigs from T&E plant species confamilial to the invasive plant in a non-destructive manner and used them to measure behavioral responses and searching time of weevils. Female weevils preferred C. officinale to all tested plant species in dual-choice bioassays using either olfactory or visual cues in a modified y-tube device. Furthermore, female weevils were repelled by the combined olfactory and visual cues from all tested T&E plant species in a dual-choice test against controls (e.g., purified air in an empty arm), indicating that it would be extremely unlikely for the weevil to attack any of these species upon release in the United States. Principal component analysis based on 61 volatile organic compounds effectively separated the five confamilial plant species and C. officinale, corroborating the results of behavioral bioassays. We conclude that studies on pre-alighting host selection behavior and the underlying physiological mechanisms of how organisms select host plants they exploit can aid in environmental safety testing of weed biological control agents.

Controlled release herbicide formulation for effective weed control efficacy.

Controlled release formulation (CRF) of herbicide is an effective weed management technique with less eco-toxicity than other available commercial formulations. To maximise the effectiveness of CRFs however, it is crucial to understand the herbicide-releasing behaviour at play, which predominately depends on the interaction mechanisms between active ingredients and carrier materials during adsorption. In this study, we investigated and modelled the adsorption characteristics of model herbicide 2,4-D onto two organo-montmorillonites (octadecylamine- and aminopropyltriethoxysilane-modified) to synthesise polymer-based CRFs. Herbicide-releasing behaviour of the synthesised CRF microbeads was then analysed under various experimental conditions, and weed control efficacy determined under glasshouse conditions. Results revealed that adsorption of 2,4-D onto both organo-montmorillonites follows the pseudo-second-order kinetics model and is predominately controlled by the chemisorption process. However, multi-step mechanisms were detected in the adsorption on both organoclays, hence intra-particle diffusion is not the sole rate-limiting step for the adsorption process. Both organoclays followed the Elovich model, suggesting they have energetically heterogeneous surfaces. Herbicide-releasing behaviours of synthesised beads were investigated at various pH temperatures and ionic strengths under laboratory and glasshouse conditions. Furthermore, weed control efficacy of synthesised beads were investigated using pot studies under glasshouse condition. Desorption studies revealed that both synthesised microbeads have slow releasing behaviour at a wide range of pHs (5-9), temperatures (25-45 °C), and ionic strengths. The results also revealed that synthesised microbeads have excellent weed control efficacy on different broad-leaf weed species under glasshouse conditions.

Weed biology and management in the multi-omics era: Progress and perspectives.

Weeds pose a significant threat to crop production, resulting in substantial yield reduction. In addition, they possess robust weedy traits that enable them to survive in extreme environments and evade human control. In recent years, the application of multi-omics biotechnologies has helped to reveal the molecular mechanisms underlying these weedy traits. In this review, we systematically describe diverse applications of multi-omics platforms for characterizing key aspects of weed biology, including the origins of weed species, weed classification, and the underlying genetic and molecular bases of important weedy traits such as crop-weed interactions, adaptability to different environments, photoperiodic flowering responses, and herbicide resistance. In addition, we discuss limitations to the application of multi-omics techniques in weed science, particularly compared with their extensive use in model plants and crops. In this regard, we provide a forward-looking perspective on the future application of multi-omics technologies to weed science research. These powerful tools hold great promise for comprehensively and efficiently unraveling the intricate molecular genetic mechanisms that underlie weedy traits. The resulting advances will facilitate the development of sustainable and highly effective weed management strategies, promoting greener practices in agriculture.

Effects of varying tillage practices and weed control methods on the efficacy of infiltration models.

Agricultural land preparation and weed control techniques are essential farm management tools that affect the dynamics of soil water infiltration and the estimation accuracy of infiltration models. To analyse the interaction effect of tillage and weed control methods on the changes in soil physical properties and the efficacy of infiltration models, an experiment was conducted on a sandy clay loam forest ochrosol at Hodzo near Ho in Ghana. Four tillage systems (No Tillage [NT], Reduced Tillage [RT], Plough + Harrow + Ridging [PHR], and Deep Tillage + Plough + Harrow + Ridging [DPHR]) and three weed control methods (Hoeing [H], Machete [MAT] and No Weeding [NW]) were employed. The study also tested the reliability of the models (Kostiakov, Philip, and Horton) using the goodness of fit statistical criteria: Root mean squared error (RMSE), Mean absolute error (MAE), Coefficient of determination (R2), and Nash-Sutcliffe efficiency (NSE). The results show that conservation tillage systems (CsT) and conventional tillage systems (CT) with MAT weeding treatments recorded the highest moisture content across the studied soil profile, especially for NT x MAT (11.189%) which was significant (p < 0.05) in the 15-30 cm layer; the lowest were observed in the CsT and CT with H weeding interactions, especially for the DPHR x H (8.086%). Comparing the interaction effect on the soil infiltration, the highest mean infiltration rate was significant (p < 0.05) under the NT X H treatment combination whilst the lowest infiltration rate was recorded in the DPHR X H and PHR X NW treatment combinations. The efficiency of the fitting models (Kostiakov > Horton > Philip) highly prioritised the soil tillage operations and weed management under the treatments DPHR x MAT > DPHR x NW > DPHR x H > RT x MAT > PHR x NW > PHR x MAT > NT x NW > RT x MAT > PHR x H > RT x H > NT x MAT > RT x NW > NT x H in that order. The trend shows that the increase in tillage intensity and the decrease in weed management intensity induce the quality of the estimation process and vice versa. The study, therefore, identified the use of machete (MAT) with DPHR under the Kostiakov model as the efficient land management for modelling the cumulative infiltration characteristics of the sandy clay loam ochrosols of the study area.