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The intelligent control of precision planting can detect and regulate the operation quality of the planter in real time, which plays an important role in improving the operation quality of the planter and the yield of the corn. In this paper, the control system of a corn precision planter is designed to realize the operating quality monitoring and electric driving of the seed-metering device. The planting quality is calculated by the time interval between the neighboring falling seeds, instead of the plant spacing, to improve the operational efficiency of the system. At the same time, the forward speed of the planter is obtained by radar, which is used to accurately match the speed of the seed-metering device with the forward speed of the planter. The velocity error of the radar is analyzed, and the relevant relationship of the radar output frequency and forward speed is established. Comparative test results of this system and the JPS-12 test bench show that the detection performance of the system is reliable, and the maximum detection error of the quality parameters is less than 2.88%. Field experiments were carried out to verify the operational performance of the control system. Two speed sensors, radar and GPS, were chosen to study the effect of speed measuring on the performance of the control system. We found that speed measuring has a significant effect on planting performance. The qualified parameters of radar were significantly higher than those of GPS, at a forward speed of 6–12 km/h. The qualification feeding index (QFI) of radar was 0.51%, 0.67%, and 2.05% higher than that of GPS at speeds of 6, 8, 10, and 12 km/h. The precision index (PREC) of radar was 17.60%, 5.44%, 16.81%, and 17.30% lower than that of GPS. Therefore, the control system based on the radar speed measurement developed in this paper can significantly improve the operating quality of the planter. [ABSTRACT FROM AUTHOR]

Chromosome elimination resulting in haploids is achieved by rapid loss of chromosomes from one parent during the zygote stage and is an important procedure to produce doubled haploid (DH) lines in plants. During crosses between an emasculated wheat (Triticum aestivum L.) and maize (Zea mays L.) as pollen donors, the complete loss of maize chromosomes results in wheat haploid embryos. Through embryo rescue and chromosome doubling processes, pure lines with stable traits can be quickly obtained. The technique is called the "Wheat × Maize System". Although this technology is not new, it remains a practical approach to date. In order to optimize and improve this technology and to achieve its maximum potential in the winter wheat area of China, this paper reviews the previous and ongoing research and technical procedures for the production of wheat DH lines via the maize pollen induction and presents outlooks on DH research and its application in wheat breeding. [ABSTRACT FROM AUTHOR]

Successive years of straw mulching and returning straw to the fields in Northeast China have made strip-tillage necessary, and reasonable strip-tillage operations can create conditions for crop growth. However, there are limited research studies on the related equipment applicable to this area. In this paper, an automatic control strip-tillage machine is designed. According to the conventional planting pattern of maize in this region, the operative processes of the machine were determined, and a suitable strip seedbed structure could then be constructed under straw mulching conditions. The type of coulters and the structural parameters of the V-type soil-crushing wheel were determined through theoretical analysis. Based on the air spring and electric linear actuator, the plowing depth control system and the straw width control system were developed, respectively, so as to improve the stability of the machine operation. Field tests showed that when the forward speed, tillage depth, and theoretical width were 6–12 km/h, 6–12 cm, and 18–24 cm, respectively, the straw clearing rate, soil crushing rate, and tillage depth and breadth stability were higher than 90%, and the soil flatness was less than 2 cm. All the indexes satisfied the agronomic and technological requirements of corn cultivation. The results of this study can provide equipment and technical support for the further popularization of conservation tillage technology. [ABSTRACT FROM AUTHOR]

Agricultural environment mapping is the premise of the autonomous navigation of agricultural robots. Due to the undulating terrain and chaotic environment, it is challenging to accurately map the environmental maize field using existing LOAM (LiDAR odometry and mapping) methods. This paper proposes a LOAM method based on maize stalk semantic features for 6-DOF (degrees of freedom) pose estimation and field mapping with agricultural robots operating in a dynamic environment. The piecewise plane fitting method filters the ground points for the complex farmland terrain. To eliminate the unstable factors in the environment, we introduce the semantic information of maize plants into the feature extraction. The regional growth method segments the maize stalk instances, the instances are parameterized to a line model, and the optimization method calculates the pose transformation. Finally, the mapping method corrects the drift error of the odometry and outputs the maize field map. This paper compares our method with the GICP and LOAM methods. The trajectory relative errors of our method are 0.88%, 0.96%, and 2.12%, respectively, better than other methods. At the same time, the map drawn by our method has less ghosting and clearer plant edges. The results show that our method is more robust and accurate than other methods due to the introduction of semantic information in the environment. The mapping of corn fields can be further used in precision agriculture. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high subjectivity, frequent error occurrence and easy damage of traditional corn seed identification methods, this paper combines deep learning with machine vision and the utilization of the basis of the Swin Transformer to improve maize seed recognition. The study was focused on feature attention and multi-scale feature fusion learning. Firstly, input the seed image into the network to obtain shallow features and deep features; secondly, a feature attention layer was introduced to give weights to different stages of features to strengthen and suppress; and finally, the shallow features and deep features were fused to construct multi-scale fusion features of corn seed images, and the seed images are divided into 19 varieties through a classifier. The experimental results showed that the average precision, recall and F1 values of the MFSwin Transformer model on the test set were 96.53%, 96.46%, and 96.47%, respectively, and the parameter memory is 12.83 M. Compared to other models, the MFSwin Transformer model achieved the highest classification accuracy results. Therefore, the neural network proposed in this paper can classify corn seeds accurately and efficiently, could meet the high-precision classification requirements of corn seed images, and provide a reference tool for seed identification. [ABSTRACT FROM AUTHOR]

The maize field environment is complex. Weeds and maize have similar colors and may overlap, and lighting and weather conditions vary. Thus, many methods for the automated differentiation of maize and weeds achieve poor segmentation or cannot be used in real time. In this paper, a weed recognition model based on improved Swin-Unet is proposed. The model first performs semantic segmentation of maize seedlings and uses the resulting mask to identify weeds. U-Net acts as the semantic segmentation framework, and a Swin transformer module is introduced to improve performance. DropBlock regularization, which randomly hides some blocks in crop feature maps, is applied to enhance the generalization ability of the model. Finally, weed areas are identified and segmented with the aid of an improved morphological processing algorithm. The DeepLabv3+, PSANet, Mask R-CNN, original Swin-Unet, and proposed models are trained on a dataset of maize seedling images. The proposed Swin-Unet model outperforms the others, achieving a mean intersection over union of 92.75%, mean pixel accuracy of 95.57%, and inference speed of 15.1 FPS. Our model could be used for accurate, real-time segmentation of crops and weeds and as a reference for the development of intelligent agricultural equipment. [ABSTRACT FROM AUTHOR]

Maize is one of the most important food and feed sources at the worldwide level. Due to this importance, all the pathogens that can infect this crop can harm both food safety and security. Fungi are the most important pathogens in cultivated maize, and Fusarium spp. are one of the most important families. Reduction in yield and production of dangerous mycotoxins are the main effects of Fusarium spp. infection. Fusarium graminearum (part of the Fusarium graminearum species complex) is one the most important fungi that infect maize, and it is the causative agent of Gibberella ear rot (GER). The main characteristics of this species include its ability to infect various species and its varying infection pressures across different years. This fungus produces various harmful mycotoxins, such as deoxynivalenol, zearalenone, butanolide, and culmorin. Infection can start from silk channels or from ear wounds. In the first case, the environmental conditions are the most important factors, but in the second, a key role is played by the feeding action of lepidopteran larvae (in Europe, Ostrinia nubilalis). All these factors need to be taken into account to develop a successful management strategy, starting from cropping methods that can reduce the source of inoculum to the direct control of the fungus with fungicide, as well as insect control to reduce ear wounds. But, the most important factor that can reduce the effects of this fungus is the use of resistant hybrids. Different studies have highlighted different defensive methods developed by the plant to reduce fungal infections, like fast drying of silk and kernels, chemical compounds produced by the plant after infection, and mechanical protection from insects' wounds. The aim of this paper is to review the scientific evidence of the most important management strategies against GER in maize and to highlight the genetic basis which is behind hybrid resistance to this disease, with a focus on genes and QTLs found in studies conducted across the world and with different types of maize from tropical cultivars to European flint. [ABSTRACT FROM AUTHOR]

Accurately and effectively measuring the breaking quality of harvested corn kernels is a critical step in the intelligent development of corn harvesters. The detection of broken corn kernels is complicated during the harvesting process due to turbulent corn kernel movement, uneven lighting, and interference from numerous external factors. This paper develops a deep learning-based detection method in real time for broken corn kernels in response to these issues. The system uses an image acquisition device to continuously acquire high-quality corn kernel image data and cooperates with a deep learning model to realize the rapid and accurate recognition of broken corn kernels. First, we defined the range of broken corn kernels based on image characteristics captured by the acquisition device and prepared the corn kernel datasets. The corn kernels in the acquired image were densely distributed, and the highly similar features of broken and whole corn kernels brough challenges to the system for visual recognition. To address this problem, we propose an improved model called BCK-YOLOv7, which is based on YOLOv7. We fine-tuned the model's positive sample matching strategy and added a transformer encoder block module and coordinate attention mechanism, among other strategies. Ablation experiments demonstrate that our approach improves the BCK-YOLOv7 model's ability to learn effectively broken corn kernel features, even when high-density features are similar. The improved model achieved a precision rate of 96.9%, a recall rate of 97.5%, and a mAP of 99.1%, representing respective improvements of 3.7%, 4.3%, and 2.8% over the original YOLOv7 model. To optimize and deploy the BCK-YOLOv7 model to the edge device (NVIDIA Jetson Nano), TensorRT was utilized, resulting in an impressive inference speed of 33 FPS. Finally, the simulation system experiment for corn kernel broken rate detection was performed. The results demonstrate that the system's mean absolute deviation is merely 0.35 percent compared to that of manual statistical results. The main contribution of this work is the fact that this is the first time that a set of deep learning model improvement strategies and methods are proposed to deal with the problem of rapid and accurate corn kernel detection under the conditions of high density and similar features. [ABSTRACT FROM AUTHOR]

Maize is one of the most important economic crops in the world. The integrity of maize grains directly affects the economic value of the maize as a main commodity that depends on the percentage of grains broken (POGB) in mechanized harvesting. Decreasing grain processing breakage is key to achieving mechanical harvesting with high quality. It is difficult to ensure a low POGB, because it depends on different maize varieties, their moisture contents, threshing devices and harvester working speeds. In this paper, the effects of these factors on the POGB are investigated when the working speed of the harvester is 1.0 m·s−1, 1.5 m·s−1 and 2.0 m·s−1, respectively. The different threshing forms, including tangential-axial-flow (TAF), axial-flow with nail-tooth (AFN), axial-flow with rasp bar in big space (AFRBBS) and axial-flow with rasp bar in small space (AFRBSS), are summarized. The POGB of TAF was 5.4% and it was the lowest of four threshing devices when the working speed of the harvester was 1.0 m·s−1, which was suitable to thresh maize at a low working speed. Maize Demeiya No. 1 (DMYN1), XianDa205 (XD205), Demeiya No. 3 (DMYN3) and Hayu189 (HY189) were harvested at different harvester working speeds, and the POGB of maize DMYN1 was the lowest among the four maize varieties. The POGB increased with increases in the working speed of the harvester and the moisture content of the maize grains. The POGB reached a minimum value of 3.5% when maize XD205 with the lowest moisture content was harvested. The results can provide a reference for choosing the maize variety, its moisture content, the threshing device and the working speed of the harvester for decreasing grain processing breakage. [ABSTRACT FROM AUTHOR]

Climate change has become the biggest global challenge, being a real danger especially for crops and an inevitable threat to food security. This paper presents the results of a study conducted in the Transylvanian Plain during 2012–2021, regarding the influence of climatic factors, such as temperature, rainfall, water reserve in the soil and hours of sunshine, on the development stages and yield of maize. During 2012–2021, the soil water reserve determined for maize cultivation was above the minimum requirements (1734.8 m3 ha−1) in the spring months, but fell below this limit in the months when the water consumption for maize was the highest, but without reaching the withering index (1202.8 m3 ha−1). The hours of sunshine in the maize vegetation period have been significantly reduced from 1655.5 h (2012) to values between 1174.6 and 1296.7 h, with a significant decrease in this parameter being observed. The coefficient of determination (R2 = 0.51) shows the importance of rainfall during the period of emergence of reproductive organs in maize production. During 2019–2021, there was a decreasing trend of temperatures in May compared to the multiannual average of this month, and therefore the processes of emergence and growth of plants in the early stages were affected. During the period of the study, all parameters analyzed (temperature, rainfall, water reserve in the soil, hours of sunshine) deviated from the multiannual average, with negative variations compared to the requirements of maize. Climatic conditions, especially during the growing season, have a significant influence on the yield of a crop, especially when the interaction between several parameters is manifested. [ABSTRACT FROM AUTHOR]

Identifying the relationships between the yield of rainfed maize and planting densities as well as the hybrids used is crucial for ensuring the sustainable development of the grain industry in high latitude China. In this study, we collected 108 grain yield date points from our multiyear (2017–2020) field experiments and combined 213 data points collected from 21 published papers to appraise the impact of planting density and hybrids on maize yield. It was found that grain yield forms a curvilinear relationship with plant density as it increased from 22,500 to 112,500 plants ha−1. The optimum plant density (OPD) was determined to be 72500 plants ha−1, with a maximum maize grain yield of 10.56 Mg ha−1. The interannual variability in grain yields among hybrids with different planting densities was mainly due to the differences in dry matter (DM), especially post-silking. Grain yields increased significantly with a rise in the proportion of post-silking DM to DM at maturity. In addition, both the collected literature and our field experiments showed that the OPD was positively correlated with solar radiation accumulated during the maize growth period and with each hybrid's year of release. This study suggests that increasing plant density and selecting new hybrids with suitable growth periods are effective approaches for increasing grain yield in high latitude China. [ABSTRACT FROM AUTHOR]

Water and fertilizer play an important role in crop growth in dryland areas. It is a necessity to improve the water use efficiency (WUE) of the crop once the water resource is limited. In northern China, where there is a wide shortage of water resources, it is therefore necessary to investigate how fertilization affects the WUE of spring maize and to quantify the effects. A total of 33 published peer-reviewed papers were collected, and a meta-analysis and random forest model analysis were performed with 364 WUE comparisons, aiming to explore the effects of fertilization on the WUE of spring maize and to clarify the optimal conditions for WUE under fertilizer management. The results showed that fertilization significantly increased the WUE of spring maize by 56.72% (P < 0.01) when compared with non-fertilization. The WUE effect under the organic–inorganic fertilizer combination (MNPK) was approximately twice as high as that under inorganic fertilizer (NPK) or organic fertilizer (M). The greatest increase in WUE occurred at 0–100 kg ha−1 of nitrogen application (NA). Under environmental conditions including 7 ≤ mean annual temperature in the test year (T) ≤ 10 °C, 400 ≤ mean annual precipitation in the test year (P) ≤ 600 mm, and mean altitude (A) > 1500 m, and soil conditions including 10 ≤ soil organic matter content (SOM) ≤ 14 g kg−1 and available phosphorus (AP) < 5 mg kg−1, the fertilization optimally enhanced the WUE of spring maize when the agronomic measures of ridge–furrow planting (RFP) and mulching film (MF) were used. The random forest model analysis indicated that the influence factors (i.e., fertilizer regimes, environmental factors, soil factors, and agronomic measures) caused 65.62% of the variation in spring maize WUE effects, while in all influence factors, fertilizer types related to fertilizer regimes caused the most variation. The initial available potassium (AK) and available nitrogen (AN) of the soil were negatively correlated to the WUE effect, indicating that fertilization imposed a better effect on the WUE of spring maize when the soil was infertile. Fertilization significantly increased the WUE of spring maize, and organic and inorganic fertilizer application provided an effective measure for the sustainable development of spring maize in northern China. After clarifying the required conditions for fertilization increasing WUE, high-efficiency water use may be achieved. [ABSTRACT FROM AUTHOR]

Many studies have examined individual water-saving management practices for corn (Zea mays L.), but few studies have looked at how combinations of practices might further enhance water optimization. The research objectives of this paper were to evaluate the impact of irrigation technology, irrigation rate, and crop genetics, as well as their interactions, on silage corn yield and forage quality. Trials were conducted in three Utah locations from 2019 through 2021. The results from five site-years indicated that the best water optimization practices varied by site-year. Low-elevation sprinklers commonly applied water more efficiently, with four of the five site-years having improved or equivalent yield compared to mid-elevation sprinklers. Irrigation rate reductions and yield losses were not proportional, as a 25% irrigation reduction resulted in better silage quality and a 7% average yield loss across site-years. Further, targeted deficit irrigation (less water during vegetation and more during maturation) was inferior to a uniform deficit during all growth stages. Drought-tolerant genetics often maintained but did not improve yield in extreme water stress environments compared to non-DT genetics. No cumulative benefits were observed when combining irrigation technology, rate, and DT genetics. Irrigation technology had the greatest potential of the three factors to optimize water use in silage corn production in the Western U.S. region. [ABSTRACT FROM AUTHOR]

Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulative temperature data for multiple growth stages of summer maize, respectively. Secondly, the effective plant height (canopy height model (CHM)), effective accumulation temperature (growing degree days (GDD)), canopy vegetation index (mainly spectral vegetation index) and canopy hyperspectral features of maize were extracted, and sensitive features were screened by correlation analysis. Then, based on single-source and multi-source data, multiple linear regression (MLR), partial least-squares regression (PLSR) and random forest (RF) regression were used to construct LAI and SPAD inversion models. Finally, the distribution of LAI and SPAD prescription plots was generated and the trend for the two was analyzed. The results were as follows: (1) The correlations between the position of the hyperspectral red edge and the first-order differential value in the red edge with LAI and SPAD were all greater than 0.5. The correlation between the vegetation index, including a red and near-infrared band, with LAI and SPAD was above 0.75. The correlation between crop height and effective accumulated temperature with LAI and SPAD was above 0.7. (2) The inversion models based on multi-source data were more effective than the models made with single-source data. The RF model with multi-source data fusion achieved the highest accuracy of all models. In the testing set, the LAI and SPAD models' R2 was 0.9315 and 0.7767; the RMSE was 0.4895 and 2.8387. (3) The absolute error between the extraction result of each model prescription map and the measured value was small. The error between the predicted value and the measured value of the LAI prescription map generated by the RF model was less than 0.4895. The difference between the predicted value and the measured value of the SPAD prescription map was less than 2.8387. The LAI and SPAD of summer maize first increased and then decreased with the advancement of the growth period, which was in line with the actual growth conditions. The research results indicate that the proposed method could effectively monitor maize growth parameters and provide a scientific basis for summer maize field management. [ABSTRACT FROM AUTHOR]

Maize diseases are reported to occur often, and are complicated and difficult to control, which seriously affects the yield and quality of maize. This paper proposes an improved YOLOv5n model incorporating a CA (Coordinate Attention) mechanism and STR (Swin Transformer) detection head, CTR_YOLOv5n, to identify common maize leaf spot, gray spot, and rust diseases in mobile applications. Based on the lightweight model YOLOv5n, the accuracy of the model is improved by adding a CA attention module, and the global information acquisition capability is enhanced by using TR2 as the detection head. The average recognition accuracy of the algorithm model can reach 95.2%, which is 2.8 percent higher than the original model, and the memory size is reduced to 5.1MB compared to 92.9MB of YOLOv5l, which is 94.5% smaller and meets the requirement of being light weight. Compared with SE, CBAM, and ECA, which are the mainstream attention mechanisms, the recognition effect we used is better and the accuracy is higher, achieving fast and accurate recognition of maize leaf diseases with fewer computational resources, providing new ideas and methods for real-time recognition of maize and other crop spots in mobile applications. [ABSTRACT FROM AUTHOR]

Maize is a plant of a global cultivation range and great economic importance, which is mainly due to its high yield potential and versatile use as food, fodder, and energy source. To evaluate the effects of drip irrigation and nitrogen fertigation on maize growth in light soil in the climate conditions of Central Poland, a field experiment was carried out in 2015–2017, as a dependent split-plot design with four replications. Two factors were used: I. drip irrigation (W0—no irrigation, W1—optimal irrigation, ensuring 100% coverage of the water needs of maize during the period of increased water needs), II—method of top dressing application of two doses of nitrogen 2 × 40 kg·ha−1 (T—traditional application as broadcasted urea, F—fertigation with the use of a 6% aqueous solution of urea). The results presented in the paper concerning the absolute, relative and unitary average increases in corn grain yields under the influence of drip irrigation indicated the potential for increasing significantly the productivity of corn under the condition of optimizing the water factor. The average yield increase was 2.35 t·ha−1, varying depending on rainfall pattern: in the dry season it was 4.79, and in the wet season 1.03–1.22 t·ha−1. The application of top-dressing nitrogen fertigation resulted in a significant increase in the yield of maize grain in relation to the traditional method of nitrogen fertilization. Drip irrigation and fertigation are treatments that, ensuring the stability of maize yield over the years, contribute to an increase in plant yield by approximately 25% on average, and over 80% in the dry seasons. [ABSTRACT FROM AUTHOR]

Agricultural practices of smallholder farming systems of Latin America can play an important role in reducing their exposure to the risks associated with climate extremes. To date, however, there is no systematic analysis of scientific evidence for the extent to which these practices can provide the multiple benefits needed for smallholders to adapt to climate extremes. In this paper, we searched scientific databases to review scientific evidence of the benefit provided by twenty-six practices in crops commonly farmed by smallholders in the region and highly relevant for their food and nutrition security; namely, coffee, maize and beans. We reviewed scientific documents (n = 304) published in the period 1953–2021 to register evidence of the practices' effects on fifty-five benefits. Our analysis of these documents found measurement records (n = 924) largely based on field experiments (85%). Our results show strong evidence of the multiple benefits that some ecosystem-based practices (e.g., tree-based practices for coffee and no tillage for maize) can provide to support the adaptation to climate extremes of smallholder farming systems and enhance a farm's natural assets (e.g., biodiversity, water, soil). We also found that the majority of research on practices in the region focused more on the socioeconomic dimension (54%) rather than on the capacity of practices to improve the natural assets of a smallholder farmers or reduce the impact of climate extremes. Given these knowledge gaps, we discuss the importance of a renovated investment in research to address existing knowledge gaps. Our concluding suggestions for future research include the need for systematizing existing knowledge from different sources (e.g., peer-reviewed, gray literature, farmers, extension agencies, etc.), and to assess the extent to which these practices can provide multiple benefits for smallholder farming systems by improving their wellbeing, reducing their vulnerability to different hydroclimatic extremes while also contributing to ecosystem services provision at the landscape level. [ABSTRACT FROM AUTHOR]

Maize will continue to expand and diversify as an industrial resource and a feed and fuel crop in the near future. The United Nations estimate that in 2050 the global population will reach 9.7 billion people. In this context, food security is increasingly being discussed. Additionally, another threat to food security is global warming. It is predicted that both the quantity and the quality of crops will be seriously affected by climate change in the near future. Scientists and breeders need to speed up the process of creating new maize cultivars that are resistant to climate stress without diminishing yield or quality. The present paper provides a brief overview of some of the most important genomics tools that can be used to develop high-performance and well-adapted hybrids of maize and also emphasizes the contribution of bioinformatics to an advanced maize breeding. Genomics tools are essential for a precise, fast, and efficient breeding of crops especially in the context of climate challenges. Maize breeders are able now to develop new cultivars with better traits more easily as a result of the new genomic approaches, either by aiding the selection process or by increasing the available diversity through precision breeding procedures. Furthermore, the use of genomic tools may in the future represent a way to accelerate the processes of de novo domestication of the species. [ABSTRACT FROM AUTHOR]

This paper aims to examine the effect of various nitrogen (N) supply treatments on the date of emergence of maize hybrids classified in different age groups. The study site was at the University of Debrecen's Látókép Experiment Station in Hungary. The date of emergence of the tested maize hybrids was monitored under control (0 kg N ha−1), 120 kg ha−1 N + PK, and 300 kg ha−1 N + PK nutrient levels in a long-term field experiment. In 2020, maize hybrids (H1 = FAO 490; H2: FAO 420–440; H3 = FAO 420; H4 = 490; H5 = 320–340; H6 = FAO 350–370) growing under natural precipitation supply conditions without irrigation were included in the study. During the days of emergence, different moisture, protein, oil, starch, and yield production levels were observed, according to the variance analysis. In diverse maize hybrids, increasing or decreasing fertilizer treatment resulted in diverse productivity metrics. Regression analysis revealed that the day of emergence had a greater impact on protein, moisture, starch, and oil content than N fertilizer; however, yield production was influenced by N fertilization, rather than day of emergence. Regarding productivity parameters, this study suggests that H1 has the best productivity until the fourth day of emergence. [ABSTRACT FROM AUTHOR]

This paper is focused on intervention scenarios for smallholder potato value chains in Kenya that improve yields, reduce losses, minimize greenhouse gas (GHG) emissions and increase economic performance and food security. For that, business models and GHG emission estimates are developed, based on the existing data, combined with relevancy and time-to-impact analysis using practical real-life information from stakeholders in the potato sector, which is the second largest agricultural crop in Kenya after maize. The results show that profitability, environmental improvement and food security can go hand in hand with realistic short-term interventions for potato production at smallholder farms. [ABSTRACT FROM AUTHOR]