Maize Plots Research Articles

Evaluation of the Maize - False basil Cultural Association in the Fight against the Fall Armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae)

In Ivory Coast, maize (Zea mays L.) is the second most cultivated cereal after rice. Despite this, maize cultivation is limited by pest pressures. Apart from the usual pests, a new enemy has arrived on the continent, the fall armyworm, Spodoptera frugiperda. To control this pest, farmers use pesticides, which are not only expensive, but also pose risks to human health and the environment. The objective of this study is to find an alternative solution to chemical control through the cultivation of maize-Ocimum gratissimum (False basil) in two ways. The first consisted in planting False basil around the maize plot as a barrier plant (P1). The second consisted of alternating maize plants with False basil plants (P2). The experimental design used was a one-factor randomized FISHER block with three treatments. The efficacy of the two association modalities was compared to that of control which was maize crop only. The results showed better control of fall armyworm in the combination than control. Of the two combination treatments, it was the P1 treatment that was effective against S. frugiperda compared to the P2 treatment and the control. Thus the infestation rate of treatment P1 (33 %) was lower than that of treatment P2 (50 %) followed by the control (70 %). In addition, it was the P1 treatment that harbored fewer armyworms (8 larvae) compared to the P2 treatment (22 larvae) and the control (35 larvae). The maize-False basil crop combination therefore appears to be beneficial than pure crops as it significantly reduces the infestation rate, damage level and harbors less fall armyworm population.

Determinants of multiple maize technology package adoption in Ethiopia: evidence from the Sidama region

The adoption of improved agricultural technology packages is vital in Ethiopia, as the expansion of cultivable land appears nearly exhausted and population size has skyrocketed. However, the country has shown a low adoption rate. Thus, this study aimed to explore the factors that hinder or facilitate the adoption of multiple maize technology packages and intensity of adoption in the northern Sidama zone of Ethiopia. A multistage sampling procedure was applied to gather cross-sectional data from 424 farm households owning 545 maize plots. A multivariate probit and two-limit Tobit models were applied to address the study objectives. The conditional probability results confirmed that maize technology packages are complementary (positive relationship). This infers that agriculture-focused policies that influence the adoption of a single component of technology packages can have a reinforcing advantage over the adoption of other technologies. Furthermore, the results from the models showed that farmers with greater experience, family size, plot size, livestock and oxen ownership, number of maize plots owned, off-farm income, access to credit, extension services and membership in institutions are more likely to adopt at least one of the improved technology packages and achieved a better status of intensity of adoption. Therefore, it is crucial to reinforce and deliver quality extension services, provide credit access, motivate youth to be involved in farming activities, inspire membership and ease the system to access inputs and technologies for broader adoption of technology packages.

Evaluating the efficiency of NDVI and climatic data in maize harvest prediction using machine learning

ABSTRACT Accurate anticipation of the maize harvest date is important in the agricultural market, as it ensures the sustainability of food production in response to the increasing global demand for food. This paper proposes a predictive model to determine the optimal harvest time in maize plots using the Normalised Difference Vegetation Index (NDVI) and climatological data. These variables were oversampled and used to train various models, including Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Machine (XGBoost), CatBoost and Support Vector Machine (SVM). Bayesian optimisation has been used to find the best hyperparameters and Shapley values to identify the variables that exert the most significant influence on the prediction in each model instance. As a result of this approach, a model with an accuracy of 92.1% and an Area Under the Curve (AUC) of 0.935 was developed. The variables that determined these results were atmospheric pressure, mean temperature, precipitation, NDVI, and precipitation.

Bagging Improves the Performance of Deep Learning-Based Semantic Segmentation with Limited Labeled Images: A Case Study of Crop Segmentation for High-Throughput Plant Phenotyping.

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation-identifying crops from the background-crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Intersection-over-Union (IoU) than the threshold method and 11% over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code.

Genetic variation in Zea mays influences microbial nitrification and denitrification in conventional agroecosystems

Abstract Background and Aims Nitrogenous fertilizers provide a short-lived benefit to crops in agroecosystems, but stimulate nitrification and denitrification, processes that result in nitrate pollution, N2O production, and reduced soil fertility. Recent advances in plant microbiome science suggest that genetic variation in plants can modulate the composition and activity of rhizosphere N-cycling microorganisms. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the rhizosphere nitrifier and denitrifier microbiome under “real-world” conventional agricultural conditions. Methods To capture an extensive amount of genetic diversity within maize we grew and sampled the rhizosphere microbiome of a diversity panel of germplasm that included ex-PVP inbreds (Z. mays ssp. mays), ex-PVP hybrids (Z. mays ssp. mays), and teosinte (Z. mays ssp. mexicana and Z. mays ssp. parviglumis). From these samples, we characterized the microbiome, a suite of microbial genes involved in nitrification and denitrification and carried out N-cycling potential assays. Results Here we are showing that populations/genotypes of a single species can vary in their ecological interaction with denitrifers and nitrifers. Some hybrid and teosinte genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbred genotypes stimulated/did not inhibit these N-cycling activities. These potential differences translated to functional differences in N2O fluxes, with teosinte plots producing less GHG than maize plots. Conclusion Taken together, these results suggest that Zea genetic variation can lead to changes in N-cycling processes that result in N leaching and N2O production, and thereby are selectable targets for crop improvement. Understanding the underlying genetic variation contributing to belowground microbiome N-cycling into our conventional agricultural system could be useful for sustainability.

Are farm input subsidies a disincentive for integrated pest management adoption? Evidence from Zambia

AbstractInput subsidy programmes (ISPs) remain a popular but contentious policy tool to promote agricultural intensification, food security and poverty reduction across Africa. Although previous studies have explored the impact of ISPs on various smallholder outcomes, no studies have analysed the impact of recent ISPs on pest management. This is particularly important given the increasing pest challenges due to climate change and the recent surge in pesticide use in low‐income countries and its associated negative consequences for human and environmental health. Thus, this study assessed the effects of ISPs on smallholder adoption of sustainable pest management practices, using data from 1048 smallholder maize plots across major maize‐producing zones of Zambia and a control function regression approach. We find consistent evidence that input subsidy receipt is negatively associated with smallholders' adoption of environmentally friendly and sustainable pest management strategies. Participation in the Zambia ISP (particularly the flexible e‐voucher system) encourages synthetic pesticide use, at the expense of sustainable practices. We also find that farmers consider synthetic pesticides and biopesticides as substitutes and are more likely to adopt sustainable pest management when they have tenure security and access to financial resources. Given the human and environmental health consequences associated with synthetic pesticide use, it would be important to leverage input subsidy schemes to promote the adoption of safer and more sustainable alternatives to synthetic pesticides. Beyond input subsidies, policies that improve tenure security and financial access for smallholders can promote the adoption of sustainable pest management practices.

Ruminal degradation of maize starch is affected by sowing date

Context Delaying sowing date of maize allows producers to achieve stabilised yield. Delays in the sowing date generate changes in the grain structure, which may affect the ruminal degradability of the maize starch. Aims The objective of this study was to evaluate the effects of maize sowing date on ruminal starch degradability, and how it is related with grain physical characteristics and source:sink ratio during the grain-filling period. Methods To study the effects of maize sowing date on grain physical characteristics and on starch in situ effective degradability (ED; 6%/h passage rate from the rumen), eight treatments were defined on the basis of eight different sowing dates in Argentina of a commercial semi-dentate maize hybrid (Dekalb 7210 VT3P), as follows: Treatment 1 (13 October), Treatments 2, 3, and 4 (4, 17 and 26 November respectively), Treatments 5, 6 and 7 (3, 15 and 28 December, respectively) and Treatment 8 (5 January). For each sowing date, three plots of maize were sown in a complete randomised-block design and harvested at grain physiological maturity. Key results Starch ED of ground (2.0-mm) grains ranged from 631 to 801 g/kg and showed a ‘J’-shape response, being lower for the sowing dates from 17 November to 3 December and highest on the sowing date of 5 January. Grain density increased until the sowing date of 17 November, and then decreased. Grain vitreousness and hardness were initially not affected, but started to decline after the sowing date of 17 November. Grain test weight was not affected until the sowing date of 15 December, when it started to decline. The source:sink ratio during the grain-filling period decreased as the sowing date was delayed. Effective starch degradability was negatively correlated with the physical characteristics evaluated and the source:sink ratio. Conclusions Delaying sowing date affected the maize source:sink ratio during the grain-filling period; this was associated with changes in grain physical characteristics and with an increase in ruminal starch degradability. Implications Delaying sowing date increases dry matter (DM) and starch effective degradability, and therefore could affect cattle weight gain and feed conversion efficiency.

Validation of farmers’ on-farm irrigation scheduling for optimal water utilization in Tigray Region, Ethiopia

Unwise use of irrigation water in water-scarce areas exacerbates water scarcity, diminishes crop yield, and leads to resource wastage. In the Tigray region of Ethiopia, where water is scarce and a determinant resource, no regional-level validation of on-farm irrigation scheduling had been conducted until now. The objectives of this study were to (i) validate farmers’ irrigation scheduling practices and (ii) optimize water consumption for increased irrigated area and number of farmers. Eight different irrigation schemes were purposively selected from the Tigray region. Primary data, acquired through field measurements, observations, and discussions, were supplemented with secondary data. Farmers' irrigation scheduling practices were predominantly of the "Fixed" type, characterized by constant irrigation intervals and amounts. Unfortunately, these practices were non-optimal, with 55% over-irrigation and 45% under-irrigation. Over-irrigation instances ranged from 1350 m3in a garlic plot within the Mesima scheme to a maximum of 1,327,067 m3in a maize plot within the Serenta scheme. The excess water could potentially be utilized to irrigate an additional area of 2 to 148 hectares, thereby benefiting 7 to 296 more farmers, respectively. Conversely, instances of under-irrigation in the Fre Lekatit scheme resulted in yield reductions of 10,445 kg for potatoes and 138,499 kg for maize. In the Tigray region, most of the schemes are semi-arid, emphasizing the imperative need for water conservation. Renovating regional-level irrigation scheduling by integrating performance assessment and enhancing water productivity at the field level, and establishing a remote-sensing-based 'Real-Time Irrigation Scheduling System' is deemed necessary to sustain dryland irrigated agriculture.

Biocontrol potential of entomopathogenic nematodes against the grey maize weevil Tanymecus dilaticollis (Coleoptera: Curculionidae) adults.

The grey maize weevil, Tanymecus dilaticollis, is a polyphagous species, which is among the most important pests of maize in Southeastern Europe. The efficacy of commercial products with two species of entomopathogenic nematodes (EPNs), Steinernema carpocapsae and Heterorhabditis bacteriophora, was investigated against adults of the grey maize weevil under laboratory conditions. Nemastar®, containing S. carpocapsae was more effective on T. dilaticollis adults than Nematop® containing H. bacteriophora, when applied uniformly to the surface of the soil, on Petri dishes containing T. dilaticollis adults. Results showed that S. carpocapsae rates of 83-333 infective juveniles/adult caused > 94% mortality in T. dilaticollis adults, whereas H. bacteriophora caused 27-61%, adult mortality, after exposure of insects to the commercial products of EPNs for 15days. The infection rates of EPNs increased with concentration applied and ranged from 70-83% and 19-64% for Nemastar® and Nematop®, respectively. Subsequent field and semi-field tests were conducted with Nemastar® (application rate of 50 million S. carpocapsae per 100m2) in maize crops with biological (mycoinsecticide Naturalis®, biofungicides and fertilizers) and chemical seed treatment (Gaucho® FS 600; active ingredient: imidacloprid) in Knezha, Bulgaria. Nematodes were found only in the dead specimens, in open plots and cages sprayed with the commercial nematode product. Nematode sprayings contributed for higher maize yields in the open maize plots in the fields with different seed treatments. We suggest that the use of powder formulation of S. carpocapsae in combination with biologically treated maize seeds can contribute to minimize the use of chemical insecticides against the grey maize weevil. The results obtained can be used as a base to further tests to ascertain the efficacy of EPNs products before they can be recommended for use in the integrated approach to T. dilaticollis management.

Integrating Entomopathogenic Fungi into Different Crop Management Practices for Fall Armyworm Management

The study was carried out to identify crop management practices favouring entomopathogenic fungi of fall armyworm (Spodoptera frugiperda J. E. Smith). An experiment was set up with 24 maize plots measuring 6 m x 5 m in Siaya, JOOUST Campus field during the September to December 2021 short rain season for identification of crop management practices favouring entomopathogenic fungi of fall armyworm. Morphological and Molecular characterization was done to estimate the diversity of entomopathogenic fungi offal armyworm larvae specimens from the treatment plots. Each plot was separated by 2 m occupied with Brachiaria CV Mulato to counter the possibility of fall armyworm migration from one treatment plot to another as recommended by the ICIPE’s fall armyworm push-pull technology. The results showed that weeding was statistically significant (0.044) on fall armyworm larvae collected while only 2 species of entomopathogenic fungi were isolated for this crop management practice. The case was different with intercropping, though not statistically significant, over 50% of the isolated species were from this management practice. This could mean that intercropping is likely to reduce FAW larvae infestation whilst increasing fungal activity. This study therefore concludes that the threat that S. frugiperda poses also calls for the need for the development and adoption of good crop management practices such as intercropping, adequate fertilization, and weeding

Combining multiple baiting cycles with digital droplet PCR optimizes description of the distribution of entomopathogenic nematodes in French maize fields

Entomopathogenic nematodes (EPNs) from the genera Steinernema and Heterorhabditis are soil-dwelling parasites that feed on insect larvae. They are valuable auxiliaries in the fight against insect pests of crops used in market gardening and through the addition of non-native EPNs. However, a better description of the natural distribution of EPNs in agricultural soils will be required to foster their use in crop management programs. The objective of this study was to develop an optimal methodology for obtaining an accurate picture of the presence and diversity of EPNs in the maize fields around Pau in South-West France. We combined different approaches for the detection of EPNs in 43 maize plots. We optimized a method for isolating EPNs directly from soil samples based on multiple baiting cycles with Galleria mellonella. With this approach, Steinernema and Heterorhabditis were isolated from 25.5% to 2.5% of the plots, respectively. We also extracted the soil nematofauna. An initial morphological identification of the EPNs present in these samples led to the detection of Steinernema and Heterorhabditis in 2.5 % and 7% of the plots, respectively. We then applied molecular detection techniques to the nematofauna samples, focusing on Steinernema. We detected Steinernema in 16.5% of plots by quantitative real-time PCR (qPCR) and in 35% of plots by digital droplet PCR (ddPCR). We propose a combination of multiple baiting cycles on soil samples with soil nematofauna extraction followed by ddPCR to optimize the detection of EPNs for the analysis of their distribution in agricultural soils.

Expression of Cry1Ab/2Aj Protein in Genetically Engineered Maize Plants and Its Transfer in the Arthropod Food Web

While transgenic Bacillus thuringiensis (Bt) maize provides pest resistance and a reduced application of chemical pesticides, a comprehensive environmental risk assessment is mandatory before its field release. This research determined the concentrations of Bt protein in plant tissue and in arthropods under field conditions in Gongzhuling City, northeastern China, to provide guidance for the selection of indicator species for non-target risk assessment studies. Bt maize expressing Cry1Ab/2Aj and non-transformed near-isoline were grown under identical environmental and agricultural conditions. Cry1Ab/2Aj was detected in plant tissues and arthropods collected from Bt maize plots during pre-flowering, flowering, and post-flowering. The expression of Cry1Ab/2Aj varied across growth stages and maize tissues, as well as in the collected arthropods at the three growth stages. Therefore, representative species should be chosen to cover the whole growing season and to represent different habitats and ecological functions. Dalbulus maidis (Hemiptera: Cicadellidae), Rhopalosiphum padi (Hemiptera: Aphididae), Heteronychus arator (Coleoptera: Scarabaeidae), and Somaticus angulatus (Coleoptera: Tenebrionidae) are suitable non-target herbivores. Propylea japonica (Coleoptera: Coccinellidae), Paederus fuscipes (Coleoptera: Staphylinidae), Chrysoperla nipponensis (Neuroptera: Chrysopidae), and spiders are suggested predators. Apis cerana and Apis mellifera ligustica (both Hymenoptera: Apidae) represent pollinators and Folsomia candida (Collembola: Isotomidae) decomposers.

Improving evapotranspiration computation with electrical resistivity tomography in a maize field

AbstractHydrogeophysical methods have been increasingly used to study subsurface soil–water dynamics, yet their application beyond the soil compartment or the quantitative link to soil hydraulic properties remains limited. To examine how these methods can inform model‐based evapotranspiration (ET) calculation under varying soil water conditions, we conducted a pilot‐scale field study at an experimental maize plot with manipulated irrigation treatments. Our goal was to develop a workflow for (1) acquiring and inverting field electrical resistivity tomography (ERT) data, (2) correlating ERT to soil hydraulic properties, (3) spatially characterizing soil water stress that feeds into ET modeling (the FAO‐56 model), and (4) evaluating the performance of ERT‐based ET computation. Our results showed that ERT was able to capture decimeter‐scale soil water content (SWC) dynamics from root water uptake and irrigation manipulation and the contrast of soil water stress between deficiently and fully irrigated maize. We also demonstrated the flexibility of using ERT to spatially integrate soil water stress in the soil volume of interest, which could be adjusted based on different crops and plot layouts. The integration of the ERT datasets into ET modeling provided insights into the spatial heterogeneity of the subsurface that has been challenging for point‐based sensing, which can further our understanding of the hydraulic dynamics in the soil‐plant‐atmosphere continuum.

Developing a Localized Emergence Model of Echinochloa crus-galli to Predict Early Post-Herbicide Effectiveness in Maize

In order to achieve integrated weed management, precision timing is just as important an aspect to consider as spatial precision: the stage of the plant at the time of action will impact its successful control or survivability and thus the selection pressure for herbicide resistance. Weed emergence models are one aspect of this precision timing, but they are yet underutilized. One critique has been that models based on bare ground emergence are not always validated with emergence in the crop, and yet also residual herbicides and their timing may also affect the model. In this work, we compare emergence of Echinochloa crus-galli on bare ground and in maize and the impact of early post-residual herbicides at several timings. Experiments on bare ground and in maize were set in Prague, Czech Republic, in 2021, 2022, and 2023. Bare-ground quadrats of 0.25 m2 were randomly assigned in a space of 100 m2. Maize plot treatments of four herbicides at each of five timings were assigned in a randomized complete block design (dimethenamid-P at 1008 g ai ha−1, pethoxamid at 1200 g ai ha−1, isoxaflutole at 96 g ai ha−1, and mesotrione at 480 g ai ha−1). Three 0.25 m2 quadrats were enumerated in each plot from first emergence to full canopy closure (May to July). Model fit to emergence from the bare-ground plots using thermal time with a base temperature of 10 °C resulted in an AIC of −494. The bare-ground model was validated with emergence from the nontreated control plots in maize in 2022 and 2023, which accounted for over 85% of the variability in observed emergence. At canopy closure, total emergence since herbicide application was affected by herbicide and application timing. All herbicides at all timings reduced the emergence after application except for mesotrione. When beginning thermal time from the day of application, the emergence pattern after mesotrione application at all timings could be modeled with a single equation. E. crus-galli had a reliable emergence pattern within a local population; the predictive model created using bare-ground plots adequately predicted emergence in maize. This information can be used to time herbicides to coincide with the most effective moment in the flush in areas where E. crus-galli is the driver weed species.

Is dirt cheap? The economic costs of failing to meet soil health requirements on smallholder farms

AbstractAgricultural productivity is hindered in smallholder farming systems due to several factors, including farmers’ inability to meet crop‐specific soil requirements. This article focuses on soil suitability for maize production and creates multidimensional soil suitability profiles of smallholder maize plots in Uganda while quantifying forgone production due to cultivation on less‐than‐suitable land and identifying groups of farmers that are disproportionately impacted. The analysis leverages the unique socioeconomic data from a subnational survey conducted in Eastern Uganda, inclusive of plot‐level, objective measures of maize yields and soil attributes. Stochastic frontier models of maize yields are estimated within each soil suitability class to understand differences in returns to inputs, technical efficiency, and potential yield. Farmers cultivating highly suitable soil have the potential to increase their observed yields by as much as 86%, while those at the opposite end of the suitability distribution (i.e., with marginally suitable land) operate closer to the production frontier and can only increase yields by up to 59%, given the current technology set. There is heterogeneity in potential gains across the wealth distribution, with poorer households facing more heavily constrained potential.

Organic soil fertility management practices for the management of fall armyworm, Spodoptera frugiperda (J.E. Smith), in maize.

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), is a polyphagous pest native to the Americas. It attacks several crops but in particular causes significant damage to maize, which is a staple crop in Africa. Organic soil amendments have an impact on the physical, chemical, and biological properties of soil, which enhance plant resistance to or tolerance of insect pests and also promote a diverse population of natural enemies of the pest. However, the practices followed for the management of crop residue and animal manure affect their use as organic soil amendments. A field experiment was conducted to evaluate the effect of maize residue and cattle manure incorporation into soil on FAW in the Mana and Omo Nada districts of the Jimma zone, southwest Ethiopia, during the 2018/19 cropping season. Treatment involved three factors: five different levels of maize residue retention (0%, 25%, 50%, 75%, and 100%), different cattle manure storage systems (control, open, steel roof, and grass roof), and two different districts (Mana and Omo Nada). These variables were organized in a randomized complete block design and replicated three times. The infestation and damage ratings were collected from 30 days after planting at 20-day intervals. The results indicated that maize plots with retained crop residue had a significant reduction in FAW infestation compared with plots without maize residue (control) in both study districts. Furthermore, manure-fertilized plants had a lower percentage of FAW infestation when compared with maize plots without cattle manure in both study districts. The lowest severity of FAW infestation was recorded in a plot with 100% of residue incorporated and treated with cattle manure stored under a grass roof in the Mana district. Therefore, conventional tillage with 100% maize residue incorporation and the application of cattle manure stored under a grass roof showed the best result for reducing FAW infestation in maize. However, further studies are important to determine the effect of treatments over seasons and locations on FAW infestation and maize yields.

Assessing the impact of Gliricidia agroforestry-based interventions on crop nutritional, antinutritional, functional, and mineral compositions in eastern Province, Zambia

ABSTRACT Agroforestry practices improve soil health which in turn improves crop nutrient concentrations and quality. This study examined how the agroforestry tree Gliricidia sepium intercropped with soybean, groundnuts, or maize affects crop nutrient compositions. The study was conducted in five Zambian chiefdoms for three crop-growing seasons (2019–2022) on 13 farmer-led demonstration trial sites. Seven treatments were tested that included maize, soybean, and groundnut plots with and without Gliricidia interventions. Grain samples were analyzed for crop nutrient contents using standard laboratory methods. Results showed that the treatments significantly (P < 0.05) improved maize nutritional properties except for crude fiber, total carbohydrate, and metabolizable energy. G. sepium intercropping with maize and soybean decreased the antinutritional contents and displayed better functional qualities. All elemental mineral components (except potassium, calcium, and sodium) were higher in the Gliricidia + maize intercrop than in the control treatment. The Gliricidia+soybean intercrop had lower mean mineral concentrations than the control (soybean only) except for Mg, Cu, and Zn. The Giliricidia+groundnut intercrop significantly increased groundnut mineral components except for Nitrogen, Phosphorus, Potassium, and Iron. It can be concluded that G. sepium intercropped with maize, soybean, and groundnuts significantly improved the crops’ nutritional quality.

Can I speak to the manager? The gender dynamics of decision-making in Kenyan maize plots.

Gender and social inclusion efforts in agricultural development are focused on making uptake of agricultural technologies more equitable. Yet research looking at how gender relations influence technology uptake often assumes that men and women within a household make farm management decisions as individuals. Relatively little is understood about the dynamics of agricultural decision-making within dual-adult households where individuals' management choices are likely influenced by others in the household. This study used vignettes to examine decision-making related to maize plot management in 698 dual-adult households in rural Kenya. The results indicated a high degree of joint management of maize plots (55%), although some management decisions-notably those related to purchased inputs-were slightly more likely to be controlled by men, while other decisions-including those related to hiring of labor and maize end uses-were more likely to be made by women. The prevalence of joint decision-making underscores the importance of ensuring that both men's and women's priorities and needs are reflected in design and marketing of interventions to support maize production, including those related to seed systems, farmer capacity building, and input delivery.

Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network

Maize is a vital crop in China for both food and industry. The nitrogen content plays a crucial role in its growth and yield. Previous researchers have conducted numerous studies on the issue of the nitrogen content in single maize plants from a regression perspective; however, partition management techniques of precision agriculture require plants to be divided by zones and classes. Therefore, in this study, the focus is shifted to the problems of plot classification and graded nitrogen estimation in maize plots performed based on various machine learning and deep learning methods. Firstly, the panoramic unmanned aerial vehicle (UAV) images of maize farmland are collected by UAV and preprocessed to obtain UAV images of each maize plot to construct the required datasets. The dataset includes three classes—low nitrogen, medium nitrogen, and high nitrogen, with 154, 94, and 46 sets of UAV images, respectively, in each class. The training set accounts for eighty percent of the entire dataset and the test set accounts for the other twenty percent. Then, the dataset is used to train models based on machine learning and convolutional neural network algorithms and subsequently the models are evaluated. Comparisons are made between five machine learning classifiers and four convolutional neural networks to assess their respective performances, followed by a separate assessment of the most optimal machine learning classifier and convolutional neural networks. Finally, the ShuffleNet network is enhanced by incorporating SENet and improving the kernel size of the Depthwise separable convolution. The findings demonstrate that the enhanced ShuffleNet network has the highest performance; its classification accuracy, precision, recall, and F1 scores were 96.8%, 97.0%, 97.1%, and 97.0%, respectively. The RegNet, the optimal model among deep learning models, achieved accuracy, precision, recall, and F1 scores of 96.4%, 96.9%, 96.5%, and 96.6%, respectively. In comparison, logistic regression, the optimal model among the machine learning classifiers, attained accuracy of 77.6%, precision of 79.5%, recall of 77.6%, and an F1 score of 72.6%. Notably, the logistic regression exhibited significant enhancements of 19.2% in accuracy, 17.5% in precision, 19.5% in recall, and 24.4% in the F1 score. In contrast, RegNet demonstrated modest improvements of 0.4% in accuracy, 0.1% in precision, 0.6% in recall, and 0.4% in the F1 score. Moreover, ShuffleNet-improvement boasted a substantially lower loss rate of 0.117, which was 0.039 lower than that of RegNet (0.156). The results indicated the significance of ShuffleNet-improvement in the nitrogen classification of maize plots, providing strong support for agricultural zoning management and precise fertilization.

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling.

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.