Search Results (40)

Grasslands are crucial for sequestering carbon underground, but disturbances like ploughing can lead to significant soil organic carbon (SOC) loss as CO₂, a potent greenhouse gas. Thus, managed grasslands should be maintained to minimize GHG emissions. A field study was carried out to investigate how varying sward diversity influences soil respiration following the ploughing of temporary grassland. This study investigated the extent of CO₂ emissions from different species mixtures immediately after ploughing, as well as C losses when straw was added to plots, over a 142-day period. The species mixture treatments consisted of a binary mixture (BM), a tertiary mixture (TM), and a complex mixture (CM), which were compared to two bare plot treatments, one of which was also ploughed. The highest CO₂ flux occurred immediately after ploughing and was observed in the BM treatment (1.99 kg CO₂-C ha⁻¹ min⁻¹). Accumulated CO₂ emissions ranged from 0.4 to 14.8 t CO₂ ha⁻¹. The ploughing effect on CO₂ emissions was evident for bare soils, as ploughing increased soil aeration, which enhanced microbial activity and accelerated the decomposition rate of soil organic matter. However, different mixtures did not affect the C turnover rate. Adding straw to treatments resulted in 43% higher CO₂ emissions compared to bare plots. The BM treatment likely induced a higher priming effect, suggesting that the incorporated straw, under different sward residues, influenced CO₂ emissions more than the mechanical disturbance caused by ploughing. Findings suggest that using complex species mixtures can be recommended as a strategy to reduce CO₂ emissions from incorporated biomass and minimize the priming effect of native soil carbon. Full article

A better understanding of the role of plant composition and N cycle on agroecosystems is necessary, as these will be affected by future developments in agriculture intensification. To explore the effect of plant diversity on yield and carbon (C) and nitrogen (N) balances in forage mixtures, identifying potential co-benefits between functions. We analyzed results from a field experiment where plants of three forage species (a grass, a legume, and a non-legume forb) were cultivated in monocultures and mixtures. Three years after sward establishment, dry matter yield, together with δ¹⁵N, δ¹³C, and C and N content in plant and soil material were measured. In addition, we analyzed a second scenario to investigate the effect of fertigation with pig slurry (δ¹⁵N = +8.4‰) on the C and N balances of forage species. Results support the hypothesis that C and N allocation is affected by plant diversity. Plant composition affected N source (% N derived from air, % N derived from soil, and % N transferred in mixtures). In addition, sown diversity increased yield and modulated C and N balances. The δ¹⁵N of samples was affected by both plant composition and fertigation. These results are consistent with previous work showing strong plant composition effects on N-balances, and the potential role that legumes play in enhancing nitrogen sources (derived from the atmosphere) into forage mixture systems. This study contributes to the prediction of suitable sown plant community composition and N management for the optimum agriculture with increased productivity and at the same time reduced environmental impact. Full article

The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency of utilization (cuts) in grasslands. Therefore, the main goal of the review is to examine the techniques for using PA applications to monitor productivity and quality in forage and grasslands. To achieve this, the authors discuss several monitoring technologies for biomass and plant stand characteristics (including quality) that make it possible to adopt digital farming in forages and grassland management. The review provides an overview about mass flow and impact sensors, moisture sensors, remote sensing-based approaches, near-infrared (NIR) spectroscopy, and mapping field heterogeneity and promotes decision support systems (DSSs) in this field. At a small scale, advanced sensors such as optical, thermal, and radar sensors mountable on drones; LiDAR (Light Detection and Ranging); and hyperspectral imaging techniques can be used for assessing plant and soil characteristics. At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. It is known that the delineation of sward heterogeneity is more difficult in mixed grasslands due to spectral similarity among species. Thanks to Diversity-Interactions models, jointly assessing various species interactions under mixed grasslands is allowed. Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. The authors recommend a future research direction for the inclusion of costs and economic returns of digital technologies for precision grasslands and fodder production. Full article

Effective agricultural management hinges upon timely decision-making. Here, we evaluated whether drone and satellite imagery could improve real-time and remote monitoring of pasture management. Using unmanned aerial systems (UAS), we quantified grassland biomass through changes in sward height pre- and post-grazing by sheep. As optical spectral data from Sentinel-2 satellite imagery is often hindered by cloud contamination, we assessed whether machine learning could help improve the accuracy of pasture biomass prognostics. The calibration of UAS biomass using field measurements from sward height change through 3D photogrammetry resulted in an improved regression (R² = 0.75, RMSE = 1240 kg DM/ha, and MAE = 980 kg DM/ha) compared with using the same field measurements with random forest-machine learning and Sentinel-2 imagery (R² = 0.56, RMSE = 2140 kg DM/ha, and MAE = 1585 kg DM/ha). The standard error of the mean (SEM) for the field biomass, derived from UAS-measured sward height changes, was 1240 kg DM/ha. When UAS data were integrated with the Sentinel-2-random forest model, SEM reduced from 1642 kg DM/ha to 1473 kg DM/ha, demonstrating that integration of UAS data improved model accuracy. We show that modelled biomass from 3D photogrammetry has significantly higher accuracy than that predicted from Sentinel-2 imagery with random forest modelling (S2-RF). Our study demonstrates that timely, accurate quantification of pasture biomass is conducive to improved decision-making agility, and that coupling of UAS with satellite imagery may improve the accuracy and timeliness of agricultural biomass prognostics. Full article

The extensive identification of mowing events on a territory holds significant potential to help monitor shifts in biodiversity and contribute to assessing the impacts of drought events. Additionally, it provides valuable insights into farming practices and their consequential economic and ecological effects. To overcome challenges in obtaining reference grazing information directly from the field, this study introduces a novel methodology leveraging the compressed sward height (CSH) derived from Sentinel-1, Sentinel-2, and meteorological data, boasting an accuracy of 20 mm. Our central hypothesis posits that the mowing status of a parcel can be automatically discerned by analyzing the distribution and variation of its CSH values. Employing a two-step strategy, we first applied unsupervised algorithms, specifically k-means and isolation forest, and subsequently amalgamated the outcomes with a partial least squares analysis on an extensive dataset encompassing 194,657 pastures spanning the years 2018 to 2021. The culmination of our modeling efforts yielded a validation accuracy of 0.66, as ascertained from a focused dataset of 68 pastures. Depending on the studied year and with a threshold fixed at 0.50, 21% to 57% of all the parcels in the Wallonia dataset were tagged as mown by our model. This study introduces an innovative approach for the automated detection of mown parcels, showcasing its potential to monitor agricultural activities at scale. Full article

Water shortage, one of the main limiting factors for plant growth and development, can be alleviated by an adequate nutrient supply. The effect of different nitrogen (N), phosphorus (P) and potassium (K) supply levels and their combinations was examined in different rainfall supply periods (wet, normal, dry) on a grass sward in a field experiment. Dry and fresh aboveground biomass production were primarily increased by the N–rainfall supply interaction, from 0.739 to 6.51 and from 1.84 to 21.8 t ha⁻¹, respectively, but the P–rainfall supply and N–P interactions and K treatment all had significant effects. Dry matter content was primarily influenced by the N–rainfall supply interaction, increasing in response to N in dry periods and declining in wet periods. Water use efficiency (WUE) was increased by the N–rainfall supply interaction from 28.3 to 127 kg ha⁻¹ mm⁻¹, but the N–P interaction had a similarly strong effect, and K treatment increased it in the dry period. The N, P and K contents of the aboveground biomass were increased by treatment with the corresponding element, but were also influenced by rainfall supply. The increase in biomass, mainly due to N treatment, caused the dilution of the P and K contents in grass in treatments poorly supplied with P and K. Biomass production and WUE were significantly improved up to a dose of 200 kg ha⁻¹ year⁻¹ of N, up to a supply level of 153 mg kg⁻¹ of P₂O₅, and 279 mg kg⁻¹ of K₂O measured in the soil. Treating grass with the N, P and K macroelements may effectively increase biomass production and water use efficiency, but above a certain level their application is unnecessary. Full article

Meadow fescue, Schedonorus pratensis (Huds.) P. Beauv., has recently been discovered as a common but previously unknown pasture grass in the Driftless Area of the upper Mississippi River Valley, USA. Preliminary data also indicated that many meadow fescue pastures were infected with an endophytic fungus, Epichloë uncinata (W. Gams, Petrini & D. Schmidt) Leuchtm. & Schardl. Therefore, the objective of this study was to determine if the endophyte impacts agronomic fitness of the host meadow fescue. Meadow fescue plants from eight farm sites were intensively sampled, and endophyte infection levels were determined to range from 82 to 95%. Paired endophyte-infected (E+) and endophyte-free (E−) meadow fescue subpopulations from each collection site were then created, and were subsequently compared for greenhouse and field drought tolerance, forage mass, and persistence under frequent defoliation. There was no impact of the endophyte under a wide range of drought conditions for either greenhouse or field studies. Furthermore, there was a small forage-mass-enhancement effect in the E+ subpopulation for only one of the eight collection sites. The only consistent effect was an average of 9% increased ground cover (persistence) in endophyte-infected meadow fescue under frequent defoliation. As per other studies, enhanced root growth, fungal-disease resistance, and/or reduced insect feeding could be mechanisms for this increased survivorship. We conclude that the meadow fescue endophytes present in the Driftless Area do not help protect their host from drought or provide any consistent forage-growth enhancement; however, we found evidence that the endophyte provides some protection against frequent defoliation at low residual sward heights. Full article

Saline water irrigation contributes significantly to forage yield. However, the acceptable salinity levels for saline water irrigation of tall wheatgrass remains unclear. In this study, field supplemental irrigations of transplanted-tall wheatgrass with saline drainage waters having salinities of electrical conductivity (EC_w) = 2.45, 4.36, 4.42, and 5.42 dS m⁻¹ were conducted to evaluate the effects of saline water irrigation on forage yield and soil salinization. In addition, the effects of plastic film mulching, fertilization, and saline water irrigation on sward establishment of seed-propagated tall wheatgrass were determined. Finally, a pot experiment was carried out to confirm the above field results. The results showed that two irrigations with EC_w = 2.45 and 4.36 dS m⁻¹ saline waters produced the highest dry matter yield, followed by one irrigation with EC_w = 4.42 or 5.42 dS m⁻¹. After rainfall leaching, the soil EC_1:5 was reduced by 41.7–79.3% for the saline water irrigation treatments. In combination with saline water irrigation, plastic film mulching promoted sward establishment and enhanced the plant height and dry matter yield of seed-propagated tall wheatgrass, while fertilization played a marginal role. However, two irrigations with EC_w = 7.13 and 4.36 dS m⁻¹ saline waters resulted in rates of 3.2% and 16.0% of dead plants under the mulching and no mulching conditions, respectively. Furthermore, a pot experiment demonstrated that irrigation with EC_w = 5.79 dS m⁻¹ saline water led to the lowest reduction in forage yield and the highest crude protein content in leaves. However, the plants irrigated with EC_w ≥ 6.31 dS m⁻¹ saline water enhanced soil salinity and reduced the plant height, leaf size, and gas exchange rate. Conclusively, one irrigation with EC_w ≤ 5.42 dS m⁻¹ and SAR ≤ 36.31 saline water at the end of April or early May could be acceptable for tall wheatgrass production and minimize the soil salinization risk in the coastal saline–alkaline land around the Bohai Sea. Full article

Perennial ryegrass (Lolium perenne L.) is a commonly grown pasture species in temperate agriculture, mainly serving as a primary energy source for dairy cows. However, its limited persistence often leads to missed production potential and early resowing, especially in countries that experience summer drought, e.g., Australia and New Zealand. Therefore, understanding the factors influencing perennial ryegrass pasture persistence is crucial for sustainable land management and climate resilience in pasture-based animal production systems. Significant gaps in knowledge exist regarding the factors influencing pasture persistence, as the number of conducted studies in this area remains limited. This study aimed to investigate the factors influencing the expression of persistence in perennial ryegrass populations using airborne and ground-based sensors. A field experiment was conducted in the southwest region of Victoria, Australia, involving ten commercial perennial ryegrass cultivar–endophyte combinations in two different populations. Persistence was evaluated using sensor-based and conventional pasture measurements over two consecutive autumns. The results revealed significant fixed effects of cultivar, endophyte, and environment and their interactions on persistence traits of perennial ryegrass. Cultivars Alto, Samson, and One50 exhibited high levels of persistence when infected with novel endophyte strains. Furthermore, prolonged environmental stresses were found to drive directional selection within pasture populations. The findings emphasise the importance of selecting appropriate cultivar–endophyte combinations and early detection of signs of poor persistence to optimise sward longevity and financial returns from pasture-based animal production systems. This study fills a knowledge gap regarding the factors influencing pasture persistence and provides valuable insights for sustainable pasture management strategies. Full article

The productivity of fodder legumes, based on internal sources of N, may be limited due to an insufficient supply of nutrients responsible for the efficient use of N accumulated by the crop during the growing season. Production risk occurs on soils that are naturally poor or depleted in nutrients that are decisive for the fixation and utilization of N₂ by alfalfa. This hypothesis was validated on the basis of a field experiment with an alfalfa–grass mixture carried out over three main seasons (2012−2014) on soil low in available potassium (K), calcium (Ca), and sulfur (S). The experiment involved two factors that contained two levels of applied gypsum (GYP: 0, 500 kg ha⁻¹) fertilized with P and K (POT: absolute control—AC, P60K0, P60K30, P60K60, and P60K120). In each main season of the alfalfa–grass mixture, the sward was mowed three times (three cuts). The total sward yield (TY) reached its maximum in the second main season (15.6 t DW ha⁻¹), then it significantly decreased. The sward yield of the third cut was the main driver of the TY. The content of P in the first cut, and especially P and S in the third cut of the sward, affected the N:P and P:S ratios, which, in turn, determined the productivity of the alfalfa–grass mixture. The total amount of accumulated N (TN) in the sward significantly responded to gypsum and PK fertilizers. In the first and third main seasons, the highest TN was found on the plot fertilized with both gypsum and 120 kg K₂O ha⁻¹. In the second main season, the TY was determined by PK dose, being variable in successive years. The highest total N accumulation (TN) was recorded in the second main season. It reached 504 kg N ha⁻¹ on the plots with GYP−0 and 436 kg N ha⁻¹ for GYP−500. However, the corresponding TY was 16.7 and 17.3 t DW ha⁻¹. This apparent discrepancy was due to the much higher productivity of N, which was 33.2 and 39.6 kg fodder DW ha⁻¹ TN, respectively. These two characteristics clearly indicate that the productivity of the accumulated N by the alfalfa–grass sward was significantly restricted by the shortage of P and S. The studies clearly emphasized that the sward of the alfalfa–grass mixture grown on soil depleted in available K, Ca, and S responds significantly to the combined application of gypsum and potassium, but provides effective control of the P supply, even on soil rich in available P. Full article

The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while improving pasture productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 ha) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months), with each compared with controls characterised by lighter stocking rates for longer periods (2000 DSE/ha). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica), and cocksfoot (Dactylis glomerata), and were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We invoked a machine learning model forced with Sentinel-2 imagery to quantify TSDM, standing green and dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM, green biomass and recovery. However, regenerative treatments significantly impacted litterfall and trampled material, with high-intensity grazing treatments trampling more biomass, increasing litter, enhancing surface organic matter and decomposition rates thereof. Pasture digestibility and sward uniformity were greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were greater for the 15-month spelling treatment. TSDM prognostics from machine learning were lower than measured TSDM, although predictions from the machine learning approach closely matched observed spatiotemporal variability within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3–6 months) was more conducive to increasing pasture production under high rainfall conditions, and we speculate that – in this environment - high-intensity grazing with 3-month spelling is likely to improve soil organic carbon through increased litterfall and trampling. Our study paves the way for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling the management of pastures within small fields from afar. Full article

One of the practices often mentioned to achieve climate change mitigation is the long-term cultivation of perennial plants. The objective of the study was to estimate changes in the accumulation of soil organic carbon (SOC) and its fractions in 0–10, 10–20, 20–30 cm, and within 0–30 cm soil layer of red fescue (Festuca rubra L.) swards that differ in age (5, 10 and 15 years) as well as to compare them with the arable field. Our results show that SOC accumulation at 5-year-old cultivation of red fescue is high, later this SOC increase slowed down from 71% in the 0–30 cm soil layer when land use was converted from arable field to 5-year-old sward to 1% from 10 to 15 years. The level of water extractable organic carbon (WEOC) in the 0–30 cm soil layer of swards was significantly higher compared to the arable field. The positive effect of these swards in the accumulation and stabilization of organic carbon during humification in the soil was also determined. The largest amounts of mobile humic substances (MHS) and mobile humic acids (MHA) accumulated in the 0–10 cm layer of sward soil (3.30–4.93 and 1.53–2.48 g kg⁻¹, respectively). In conclusion, the findings suggest that a conversion from arable to soil under permanent grass cover significantly improves carbon status. Full article

Long-term phosphorus (P) accumulation in agricultural soils presents a challenge for water quality improvement. P is commonly elevated in soils managed for intensive livestock production due to repeated overapplication of slurry and fertilisers. High legacy nutrient accumulations result in poor water quality via transport pathways such as surface runoff, subsurface drainage, and soil erosion. To achieve environmental water quality targets, improved management strategies are required for targeting and reducing excess agricultural P sources. Reseeding of old swards is known to improve grassland productivity and enhance overall soil health. However, soil disturbance associated with reseeding could have positive and negative impacts on other soil functions that affect the nutrient balance (including improved microbial activity, but also increasing the potential for sediment and nutrient losses). This study investigates the impact of reseeding and inversion tillage in addressing soil surface nutrient surpluses and identifies potential trade-offs between production, environment (through soil erosion and associated sediment and nutrient losses), and soil health. At a study site in the Blackwater catchment in Northern Ireland, we collected high-resolution (35 m) gridded soil samples pre- and post-reseeding for nutrient analyses and combined this with GIS-based interpolation. We found that decreases in sub-field scale surface nutrient content (0–7.5 cm depth) occurred following tillage and reseeding, but that this was spatially variable. In addition, the magnitude of changes in nutrient content was variable between P and other sampled nutrients. LiDAR-based image differencing indicated variability in the magnitude of soil erosion and sediment loss also at sub-field scale. Information on the identified deposition and erosion zones (from LiDAR analysis) was combined with mass wasting data to determine accumulation rates and losses of nutrients in-field and confirmed some of the identified patterns in soil surface nutrient content changes post-reseeding. We conclude that while inversion tillage and reseeding are essential agricultural practices, environmental trade-offs exist through potential nutrient and sediment losses. LiDAR-based image differencing was found to be a useful tool in helping to quantify these risks. Quantifying sediment and nutrient losses as a result of inversion tillage and reseeding induced soil erosion aids in understanding potential trends in water quality statuses. Full article

Precise and timely information on biomass yield and nitrogen uptake in intensively managed grasslands are essential for sustainable management decisions. Imaging sensors mounted on unmanned aerial vehicles (UAVs) along with photogrammetric structure-from-motion processing can provide timely data on crop traits rapidly and non-destructively with a high spatial resolution. The aim of this multi-temporal field study is to estimate aboveground dry matter yield (DMY), nitrogen concentration (N%) and uptake (Nup) of temperate grasslands from UAV-based image data using machine learning (ML) algorithms. The study is based on a two-year dataset from an experimental grassland trial. The experimental setup regarding climate conditions, N fertilizer treatments and slope yielded substantial variations in the dataset, covering a considerable amount of naturally occurring differences in the biomass and N status of grasslands in temperate regions with similar management strategies. Linear regression models and three ML algorithms, namely, random forest (RF), support vector machine (SVM), and partial least squares (PLS) regression were compared with and without a combination of both structural (sward height; SH) and spectral (vegetation indices and single bands) features. Prediction accuracy was quantified using a 10-fold 5-repeat cross-validation (CV) procedure. The results show a significant improvement of prediction accuracy when all structural and spectral features are combined, regardless of the algorithm. The PLS models were outperformed by their respective RF and SVM counterparts. At best, DMY was predicted with a median RMSE_CV of 197 kg ha⁻¹, N% with a median RMSE_CV of 0.32%, and Nup with a median RMSE_CV of 7 kg ha⁻¹. Furthermore, computationally less expensive models incorporating, e.g., only the single multispectral camera bands and SH metrics, or selected features based on variable importance achieved comparable results to the overall best models. Full article

Agricultural grasslands are globally important for food production, biodiversity, and greenhouse gas mitigation. Effective strategies to monitor grass sward properties, such as dry matter yield (DMY) and nitrogen concentration, are crucial when aiming to improve the sustainable use of grasslands in the context of food production. UAV-borne spectral imaging and traditional machine learning methods have already shown the potential to estimate DMY and nitrogen concentration for the grass swards. In this study, convolutional neural networks (CNN) were trained using low-cost RGB images, captured from a UAV, and agricultural reference measurements collected in an experimental grass field in Finland. Four different deep regression network architectures and three different optimizers were assessed. The best average results of the cross-validation were achieved by the VGG16 architecture with optimizer Adadelta: $r^2$ of 0.79 for DMY and $r^2$ of 0.73 for nitrogen concentration. The results demonstrate that this is a promising and effective tool for practical applications since the sensor is low-cost and the computational processing is not time-consuming in comparison to more complex sensors. Full article