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Abstract Enhancing rapid phenotyping for key plant traits, such as biomass and nitrogen content, is critical for effectively monitoring crop growth and maximizing yield. Studies have explored the relationship between vegetation indices (VIs) and plant traits using drone imagery. However, there is a gap in the literature regarding data availability, accessible datasets. Based on this context, we conducted a systematic review to retrieve relevant data worldwide on the state of the art in drone-based plant trait assessment. The final dataset consists of 41 peer-reviewed papers with 11,189 observations for 11 major crop species distributed across 13 countries. It focuses on the association of plant traits with VIs at different growth/phenological stages. This dataset provides foundational knowledge on the key VIs to focus for phenotyping key plant traits. In addition, future updates to this dataset may include new open datasets. Our goal is to continually update this dataset, encourage collaboration and data inclusion, and thereby facilitate a more rapid advance of phenotyping for critical plant traits to increase yield gains over time.

Abstract The determination of changes in soil organic carbon (SOC) content under different cropping systems is necessary for policy development oriented towards soil conservation, C sequestration, and future C credit markets. The aim of this study was to generate an open SOC dataset resulting from a systematic literature search related to the agricultural systems for Southeast Asia. The dataset has 209 articles and 4341 observations on soils of cropping systems in this region from articles published between 1987 and 2023. This dataset included different management practices, land uses, soil sampling depth, and length of SOC content assessment. In addition, inherent features of crop production reported in the experiments were included in the dataset. This dataset can be applied to quantify and compare the impact of different land uses or management practices on SOC content, providing foundational knowledge towards identifying sustainable practices. Lastly, it is a useful guide for future regional SOC sequestration policies and the development of C credit markets.

Temperature measurement is critical in many areas of research, particularly in agriculture, where it can have a significant impact on crop health and yield. Experiments such as seed germination often require numerous temperature sensors to collect extensive data. Typically, data loggers are used to store information, but market options are expensive and offer limited, non-customizable inputs (channels), creating challenges for comprehensive soil temperature monitoring. This study aims to develop a network of open-source, low-cost data loggers with multiple customizable channels for local and remote temperature data storage. The hardware includes Arduino, temperature sensors, a Real Time Clock, and a LoRa module to transmit data to a LILYGO TTGO board, which sends it to a remote MongoDB database while also storing it locally on a microSD card. In addition, a digital tool was developed to retrieve and display both current and historical readings from the MongoDB database. The total cost of this hardware is approximately US$ 72 (based on current prices) for the simplest network, which is approximately 18 % of the commercial cost. The system achieved a root mean square error (RMSE) of 1.6 °C compared to a manual sampling probe thermometer, proving it to be a reliable measurement source. The hardware developed in this study surpasses commercial options by allowing the integration of multiple sensors and emitters, creating a network of data loggers at a lower cost. In addition to the hardware, an open-source digital tool was developed to visualize historical data at no additional cost.

Accurate identification of crop phenology timing is crucial for agriculture. While remote sensing tracks vegetation changes, linking these to ground-measured crop growth stages remains challenging. Existing methods offer broad overviews but fail to capture detailed phenological changes, which can be partially related to the temporal resolution of the remote sensing datasets used. The availability of higher-frequency observations, obtained by combining sensors and gap-filling, offers the possibility to capture more subtle changes in crop development, some of which can be relevant for management decisions. One such dataset is Planet Fusion, daily analysis-ready data obtained by integrating PlanetScope imagery with public satellite sensor sources such as Sentinel-2 and Landsat. This study introduces a novel method utilizing Dynamic Time Warping applied to Planet Fusion imagery for maize phenology detection, to evaluate its effectiveness across 70 micro-stages. Unlike singular template approaches, this method preserves critical data patterns, enhancing prediction accuracy and mitigating labeling issues. During the experiments, eight commonly employed spectral indices were investigated as inputs. The method achieves high prediction accuracy, with 90% of predictions falling within a 10-day error margin, evaluated based on over 3200 observations from 208 fields. To understand the potential advantage of Planet Fusion, a comparative analysis was performed using Harmonized Landsat Sentinel-2 data. Planet Fusion outperforms Harmonized Landsat Sentinel-2, with significant improvements observed in key phenological stages such as V4, R1, and late R5. Finally, this study showcases the method’s transferability across continents and years, although additional field data are required for further validation.

Abstract In Senegal, agriculture is an important sector underpinning the socioeconomic fabric of the populace. Notably, the agricultural production in this region exhibits heightened sensitivity to climatic perturbations, particularly droughts and heat waves. This study aims to determine the resilience of different agronomic interventions for farmers practicing mixed farming that produce both crops (i.e., groundnut (Arachis hypogaea L.) and pearl millet (Pennisetum glaucum (L.) R. Br.)) and raise animals in the Groundnut Basin in Senegal, which holds historical and socioeconomic significance. To understand the current situation regarding demographics, economics, consumption behavior, and farm operations for smallholder farmers, data were comprehensively collected from government and nongovernment organizations (NGO) reports, scientific papers, organization databases, and surveys. Additionally, the Agricultural Production Systems sIMulator (APSIM) was used to understand how combinations of three planting dates, three plant densities, and six urea nitrogen (N) fertilizer rates affected the yield of pearl millet, which were used as the alternative scenarios to the baseline in the farm modeling and analyses. All the collected and generated data were used as inputs into the Farm Simulation Model (FARMSIM) to generate economic, nutritional, and risk data associated with mixed farming systems. The generated data were then used to determine the resilience of the alternative scenarios against the baseline. Initially, a multi‐objective optimization was employed to meet nutritional needs while maintaining a healthy diet at the lowest cost. Then, the scenarios that met the population's nutritional requirements were evaluated based on four economic indicators: net cash farm income (NCFI), ending cash reserves (EC), net present value (NPV), and internal rate of return (IRR). Lastly, those that passed the economic feasibility test were ranked based on risk criteria certainty equivalent (CE) and risk premium (RP). The analyses found N fertilizer rates of 0, 20, and 100 kg N ha−1 were generally economically not feasible. Additionally, medium (early‐July to late‐August) and late (late‐July to mid‐September) planting dates generally performed better than early (early‐June to late‐July) planting dates, while plant densities of 3.3 and 6.6 pL m−2 performed better than 1.1. The robust resilience approach introduced in this study is easily transferable to other regions.

Critical management interventions to target the yield potential for each environment are key to food security, increasing the resilience of current agricultural systems in Senegal. Cereal production is highly dependent on pearl millet (Pennisetum glaucum L.) rainfed systems as one of the major field crops for smallholders. This study aims to (i) quantify the production of pearl millet at the department level for the last quinquennial (until 2020), (ii) assess the impact of weather (temperature and precipitation) on the millet-based supply of cereal demand, and (iii) investigate, through crop modeling, the impact of millet-based supply of cereal demand by comparing recommended management interventions with smallholder-based strategies at the department level. Millet-based cereal supply–demand was estimated considering the observed population and the supply via the simulated pearl millet production (obtained using the APSIM-Millet model) at the department level from 1990 to 2021. High temperature and low precipitation occurrence presented a frequency of 35% across departments, leading to a reduction in millet production by roughly 6% relative to the normal average for 32 years. Adoption of recommended management showed the potential to increase the millet supply, more than doubling the current cereal supply, closing the current supply–demand gap (89 kg inhabitant−1). Achieving future cereal self-sufficiency will also require an intensification of other cereal production.

Measurement(s) biomass • N concentration Technology Type(s) field data collection • field data collection and lab analysis Factor Type(s) nitrogen nutrition index Sample Characteristic - Organism field crops Sample Characteristic - Environment agricultural systems Sample Characteristic - Location global dataset

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.

Abstract Water‐limited environments account for half of the earth's land surface, and dryland agriculture acreage is projected to expand due to climate changes. Examining typical dryland yield potentials and yield improvement measures is crucial for developing future dryland crop production systems. This case study used crop modeling to analyze dryland maize yield potential (YPd), farmers’ yield potential (YPf), and actual farm yields (Ya) from 1990 to 2015 in three counties in western Kansas (i.e., Thomas, Greeley, and Finney in the U.S. Great Plains region). The calibrated APSIM‐Maize model along with actual yields was used to estimate yield gaps attributed to: (a) agronomic factors (YG1 = YPd − YPf) and (b) socioeconomic constraints (YG2 = YPf − Ya). Observed climate conditions during maize‐growing seasons showed warming, brightening, and drying trends for all three counties in western Kansas from 1990 to 2015. Our results showed that the current actual farm yields (Ya) in western Kansas represented only 34%, 32%, and 28% of YPd in Thomas, Greeley, and Finney counties, respectively, indicating significant exploitable yield gaps. Agronomic factors (YG1) contributed the greatest to the yield gap in Greeley and Finney counties, whereas socioeconomic constraints (YG2) offered the greatest opportunity for improvement in Thomas county. Our analysis suggested that improving agronomic management could be a greater priority for further yield improvement in Greeley county, but selecting an appropriate hybrid was a greater priority in Finney county.

Abstract Objectives Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F’s genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014–2017. Data description Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Abstract Soybean [Glycine max (L.) Merr.] yield has increased over time through the introduction of new varieties and improved agronomic practices. However, seed protein concentration has decreased. We conducted field studies in 2018 and 2019 to investigate the effects of fungicide, insecticide, and foliar fertilizer application on grain yield and seed quality in two soybean maturity groups (MG). In‐season treatments targeted nutrient availability and soybean canopy duration during the seed‐filling period by fungicide, insecticide, or foliar fertilizer application at the onset of this period. Biomass samples were collected at R5, R6, and R7 and partitioned into plant parts. Year, location, and MG often influenced yield and seed composition, but foliar fungicide, insecticide, or fertilizer application had no impact on these parameters.

Abstract Objectives Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

Due to the asymptotic nature of the crop yield response curve to fertilizer N supply, the nitrogen use efficiency (NUE, yield per unit of fertilizer applied) of crops declines as the crop N nutrition becomes less limiting. Therefore, it is difficult to directly compare the NUE of crops according to genotype-by-environment-by-management interactions in the absence of any indication of crop N status. The determination of the nitrogen nutrition index (NNI) allows the estimation of crop N status independently of the N fertilizer application rate. Moreover, the theory of N dilution in crops indicates clearly that crop N uptake is coregulated by (i) soil N availability and (ii) plant growth rate capacity. Thus, according to genotype-by-environment-by-management interactions leading to variation in potential plant growth capacity, N demand for a given soil N supply condition would be different; consequently, the NUE of the crop would be dissimilar. We demonstrate that NUE depends on the crop potential growth rate and N status defined by the crop NNI. Thus, providing proper context to NUE changes needs to be achieved by considering comparisons with similar crop mass and NNI to avoid any misinterpretation. The latter needs to be considered not only when analyzing genotype-by-environment-by-management interactions for NUE but for other resource use efficiency inputs such as water use efficiency (colimitation N–water) under field conditions.

Abstract  The purpose of this study was to evaluate, during the phenological stages of inoculated soybean crop [Glycine max (L.) Merrill], the effect of different N fertilization levels and inoculation with Bradyrhizobium japonicum on N2O emissions from the soil. Gas emissions were evaluated at field conditions by the static-chamber method. Nitrogen fertilization increased N2O emissions significantly (P?2O emissions during the whole soybean growing season was the soil nitrate level (r 2?=?0.1899; P?=?0.0231). Soil moisture presented a greater control on N2O emissions between the grain-filling period and the crop commercial maturity (r 2?=?0.5361; P?r 2?=?0.29; P?=?0.019) and moisture (r 2?=?0.24; P?=?0.039) were correlated with N2O emissions during the residue decomposition period. The relationship between soil variables and N2O emissions depended on crop phenological or stubbles decomposition stages. [ABSTRACT FROM AUTHOR]