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 Two field experiments were conducted to evaluate the effect of tillage, crop residues and crop rotation on productivity of clover, maize and mung bean, at the experimental research station of the College of Agricultural Engineering Sciences - University of Baghdad in Aljadriya, Baghdad – Iraq during two seasons of 2021-2022. 1st trail was with two factors: residues (0%R and 100%R) and tillage (minimum (MT) and conventional (CT)) with four replicates. Results indicated the best values of height and dry matter yield were (70.25 cm and 5.558 Mg ha-1) for (100%R+MT) compared with (65.5 cm and 4.985 Mg ha-1) for (0%R+CT) respectively. The 2nd trail was with three factors: the same tillage and residues coupled with crop rotations (clover-maize) and (clover-mung bean). Results (representing the accumulated effect of both trials) indicated the best values of height, dry matter yield and grains and seeds  yield were (236.25 and 111.5) cm, (4.560 and 14.745) Mg ha-1, (6.840 and 3.754) Mg ha-1 for (100%R+MT) treatment compared to (210.0 and 101.25) cm, (4.048 and 11.337) Mg ha-1, (5.685 and 2.829) Mg ha-1 with (0%R+CT) for maize and mung bean respectively and under crop rotations (clover-maize) for maize and (clover- mung bean) for mung bean.

This study was conducted at two locations, Qlyasan Agricultural Research Station and Kanipanka Nursery Station, during the 2023–2024 winter season to investigate the effects of organic fertilizer and stubble height on forage yield attributes of grass pea genotypes. A split-split plot design was used with three factors: fertilizer application (F0= no fertilizer, F1= organic fertilizer), genotypes (G1: Local, G2: IGC-2011-62, G3: IGC-2011-35, G4: IGC-2011-9), and stubble height (SH1= 5cm, SH2 = 10cm). Fertilizer application significantly increased DFY in Kanipanka. Genotype significantly influenced most forage yield attributes, with G1 showing the highest values. Stubble height significantly affected GFY and DFY, particularly in Qlyasan. Kanipanka outperformed Qlyasan in key yield attributes. Cluster analysis identified two genotype groups, indicating genetic variability. Proper management of fertilizer. Interaction effects were observed for fertilizer × genotype and fertilizer × stubble height. Genotype selection and stubble height can optimize forage yield.

In perennial ryegrass ( L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait ( = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait ( = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor ( = -0.06). However, when using an MT-CV2 model, the predictive ability was positive and high ( = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

Dual-purpose sorghum response to anthracnose disease, growth, and yield was undertaken in Derashe and Arba Minch trial sites during March–June 2018 and 2019. Five sorghum varieties and Rara (local check) were arranged in a randomized complete block design with four replications. Variety Chelenko exhibited the tallest main crop plant height (430 cm) while Dishkara was the tallest (196.65 cm) at ratoon crop harvesting. Rara had a higher tiller number (main = 6.73, ratoon = 9.73) among the varieties. Dishkara and Chelenko varieties produced 50 and 10% more dry biomass yield (DBY) than the overall mean DBY, while Konoda produced 40% less. Although the anthracnose infestation was highest on the varieties Konoda (percentage severity index [PSI] = 20.37%) and NTJ_2 (PSI = 32.19%), they produced significantly ( p  < .001) higher grain yield (3.89 t/ha) than others. Under anthracnose pressure, Chelenko and Dishkara varieties are suggested for dry matter yield while NTJ_2 for grain yield production in the study area and similar agroecology.

The study was conducted to evaluate the agronomic performance, forage yield, and nutritive values of desho grass ( Pennisetum glaucifolium ) varieties under supplementary irrigation at Dambi Dollo University experimental site, Western Ethiopia. The varieties (Areka/DZF #590, Kindu kosha‐1/DZF #591, and Kulumsa/DZF #592) were arranged in a randomized complete block design with four replications. The crop water requirements 8.0 model, local climate data, forage data, and soil data were used to determine desho grass water requirements and irrigation schedules. The parameters such as agronomic performance, yield, chemical composition, and in vitro digestibility of the forage samples at 105 days after planting were determined following the standard procedures. The results showed significant differences ( p < 0.05) among varieties in most agronomic parameters except plant height and leaf width. The highest dry matter and crude protein yields were recorded from Areka/DZF #590 (12.64 and 1.35 t/ha) followed by Kulumsa/DZF #592 (11.63 and 1.12 t/ha). The chemical composition of varieties differed significantly except for hemicellulose. The in vitro digestibility of Areka/DZF #590 (624.7 g/kg) was significantly higher than Kulumsa/DZF #592 (584.3 g/kg) and Kindu kosha‐1/DZF #591 (580.9 g/kg). In conclusion, the Areka (DZF #590) desho grass variety showed superior dry matter yield and good nutritive value under supplementary irrigation conditions. Therefore, this variety is suitable and recommended for use as animal feed in the study area.

Feed insecurity remains a major limiting factor to livestock production in Ethiopia. This study evaluated the effects of fertilizer treatments on the yield, nutritional composition, and economic returns of Napier ( Pennisetum purpureum ), Desho ( Pennisetum glaucifolium Trin.), and Guinea ( Megathyrsus maximus ) grasses in northwestern Ethiopia. A factorial randomized complete block design with three replications was conducted at mid- and high-altitude sites. The treatments were: control, 100% vermicompost (VC), 70% VC + 30% urea, 30% VC + 70% urea, and 100% urea. Chemical composition parameters were analyzed, and crude protein yield per hectare (CPY t/ha) was quantified. The highest DMY (3.93 t ha⁻¹) and CPY (0.45 t ha⁻¹) of the grasses were recorded from 30% VC + 70% urea, followed by 70% VC + 30% urea. Sole VC produced moderate DMY (2.9 t ha⁻¹) but achieved the highest benefit–cost ratio (8.41). Mid-altitude conditions resulted in higher CP (9.6%) and CPY (0.37 t ha⁻¹) than high altitude. Napier grass recorded the highest CP (10.84%) and DMY (4.44 t ha⁻¹) among species. Integrated VC and urea maximized grasses yield, whereas sole VC represents a cost-efficient organic alternative for sustainable forage production and clean dairy value chains.

This study evaluated the effects of arbuscular mycorrhizal fungi inoculation on the growth and photosynthetic performance of alfalfa under different phosphorus application levels. This experiment adopts two-factors completely random design, and sets four levels of fungi application: single inoculation with Funneliformis mosseae (Fm, T 1) , single inoculation with Glomus etunicatum (Ge, T 2 ) and mixed inoculation with Funneliformis mosseae × Glomus etunicatum (Fm×Ge, T 3 ) and treatment uninfected fungus (CK, T 0 ). Four phosphorus application levels were set under the fungi application level: P 2 O 5 0 (P 0 ), 50 (P 1 ), 100 (P 2 ) and 150 (P 3 ) mg·kg -1 . There were 16 treatments for fungus phosphorus interaction. The strain was placed 5 cm below the surface of the flowerpot soil, and the phosphate fertilizer was dissolved in water and applied at one time. The results showed that the intercellular CO 2 concentration (C i ) of alfalfa decreased at first and then increased with the increase of phosphorus application, except for light use efficiency (LUE) and leaf instantaneous water use efficiency (WUE), other indicators showed the opposite trend. The effect of mixed inoculation (T 3 ) was significantly better than that of non-inoculation (T 0 ) ( p < 0.05). Pearson correlation analysis showed that C i was significantly negatively correlated with alfalfa leaf transpiration rate (T r ) and WUE ( p < 0.05), and was extremely significantly negatively correlated with other indicators ( p < 0.01). The other indexes were positively correlated ( p < 0.05). This may be mainly because the factors affecting plant photosynthesis are non-stomatal factors. Through the comprehensive analysis of membership function, the indexes of alfalfa under different treatments were comprehensively ranked, and the top three were: T 3 P 2 >T 3 P 1 >T 1 P 2 . Therefore, when the phosphorus treatment was 100 mg·kg -1 , the mixed inoculation of Funneliformis mosseae and Glomus etunicatum had the best effect, which was conducive to improving the photosynthetic efficiency of alfalfa, increasing the dry matter yield, and improving the economic benefits of local alfalfa in Xinjiang. In future studies, the anatomical structure and photosynthetic performance of alfalfa leaves and stems should be combined to clarify the synergistic mechanism of the anatomical structure and photosynthetic performance of alfalfa.

There is a pressing need for the development of sustainable and high-use efficiency nitrogen (N) fertilizer formulations to ensure food security and climate change mitigation. Recently, nanotechnology has shown a potential to contribute to sustainable agrochemicals production by the coating of organic and inorganic nanomaterials. Here we explored the use of nickel encapsulated nanoparticles with different biodegradable coatings such as: starch, polyvinyl alcohol (PVA), gum arabica, gelatin, molasses and paraffin wax (PW) to improve the physical properties of conventional N fertilizer under soil plant system. The results revealed that coating urea granules with nickel encapsulated nanoparticles significantly increased N availability and thereby the dry matter yield of Rhode grass. The coating materials reduce the dissolution and enhance the impact resistance of granules. The UC-5 treatment containing starch, PVA, molasses, PW and Ni-NPs gives the best results in the terms of release rate (77.96% of urea release after 120 min relative to 100% urea release for uncoated granule), crushing strength (70 ± 0.27 N) and Rhode grass dry matter yield (58.55 g pot−1). The results showed that the UC-5 treatment greatly enhanced soil mineral nitrogen relative to uncoated and urea coated with NiO-NPs only. Therefore, this formulation would be considered for improving plant N uptake under sustainable and clean agriculture.

Forage dry matter is the main source of nutrients in the diet of ruminant animals. Thus, this trait is evaluated in most forage breeding programs with the objective of increasing the yield. Novel solutions combining unmanned aerial vehicles (UAVs) and computer vision are crucial to increase the efficiency of forage breeding programs, to support high-throughput phenotyping (HTP), aiming to estimate parameters correlated to important traits. The main goal of this study was to propose a convolutional neural network (CNN) approach using UAV-RGB imagery to estimate dry matter yield traits in a guineagrass breeding program. For this, an experiment composed of 330 plots of full-sib families and checks conducted at Embrapa Beef Cattle, Brazil, was used. The image dataset was composed of images obtained with an RGB sensor embedded in a Phantom 4 PRO. The traits leaf dry matter yield (LDMY) and total dry matter yield (TDMY) were obtained by conventional agronomic methodology and considered as the ground-truth data. Different CNN architectures were analyzed, such as AlexNet, ResNeXt50, DarkNet53, and two networks proposed recently for related tasks named MaCNN and LF-CNN. Pretrained AlexNet and ResNeXt50 architectures were also studied. Ten-fold cross-validation was used for training and testing the model. Estimates of DMY traits by each CNN architecture were considered as new HTP traits to compare with real traits. Pearson correlation coefficient r between real and HTP traits ranged from 0.62 to 0.79 for LDMY and from 0.60 to 0.76 for TDMY; root square mean error (RSME) ranged from 286.24 to 366.93 kg·ha−1 for LDMY and from 413.07 to 506.56 kg·ha−1 for TDMY. All the CNNs generated heritable HTP traits, except LF-CNN for LDMY and AlexNet for TDMY. Genetic correlations between real and HTP traits were high but varied according to the CNN architecture. HTP trait from ResNeXt50 pretrained achieved the best results for indirect selection regardless of the dry matter trait. This demonstrates that CNNs with remote sensing data are highly promising for HTP for dry matter yield traits in forage breeding programs.

In the early stages of selection, many plant breeding programmes still rely on visual evaluations of traits by experienced breeders. While this approach has proven to be effective, it requires considerable time, labour and expertise. Moreover, its subjective nature makes it difficult to reproduce and compare evaluations. The field of automated high-throughput phenotyping aims to resolve these issues. A widely adopted strategy uses drone images processed by machine learning algorithms to characterise phenotypes. This approach was used in the present study to assess the dry matter yield of tall fescue and its accuracy was compared to that of the breeder’s evaluations, using field measurements as ground truth. RGB images of tall fescue individuals were processed by two types of predictive models: a random forest and convolutional neural network. In addition to computing dry matter yield, the two methods were applied to identify the top 10% highest-yielding plants and predict the breeder’s score. The convolutional neural network outperformed the random forest method and exceeded the predictive power of the breeder’s eye. It predicted dry matter yield with an R² of 0.62, which surpassed the accuracy of the breeder’s score by 8 percentage points. Additionally, the algorithm demonstrated strong performance in identifying top-performing plants and estimating the breeder’s score, achieving balanced accuracies of 0.81 and 0.74, respectively. These findings indicate that the tested automated phenotyping approach could not only offer improvements in cost, time efficiency and objectivity, but also enhance selection accuracy. As a result, this technique has the potential to increase overall breeding efficiency, accelerate genetic progress, and shorten the time to market. To conclude, phenotyping by means of RGB-based machine learning models provides a reliable alternative or addition to the visual evaluation of selection candidates in a tall fescue breeding programme.