Maize Cropland Research Articles

Soil erosion responses of cropland uses in contrasting slope in the Abay basin, Ethiopia

Cultivated land is the primary source of runoff and soil loss in a watershed. Quantifying the soil erosion response of dominant cereal crops at different slope gradients is vital to sustainable land use, crop management, and conservation options. This study evaluated the runoff loss (Ro), runoff coefficient (RoC), and soil loss (SL) responses of teff (Eragrostis tef), maize (Zea mays), and wheat (Triticum aestivum) cropland use under different slope conditions. During 2020 and 2021, 18 experimental erosion plots (3 m × 10 m) having 3 crops × 3 slope gradients (8%, 18%, and 32%) with two replicates were installed. Soil loss and runoff analysis were made and the significance variation among land uses and slopes was tested using ANOVA. On average, the highest Ro is recorded from teff land use (700 mm) followed by wheat (651.2 mm), and maize (570 mm) land uses. The Ro generated from the teff crop land use exceeds 18.5% and 6.9% compared to maize and wheat crop land uses (P < 0.05). The lower proportion of the rainfall was converted to runoff (RoC = 38%) under the maize crop land use, however, nearly half of the rainfall (RoC = 46.6%) became runoff in the teff crop. The average (three slope gradients) rate of SL in teff, wheat, and maize crop land uses was found to be 54.86, 45.61, and 38.27 t ha−1 year−1, respectively. Although the result shows high soil erosion in all cereal crops, cultivation of the teff crop in general and on steep slopes in particular leads to a high Ro and SL. Therefore, sustainable land management practice and setting land use policy are recommended, particularly for teff cultivation.

Adopting yield-improving practices to meet maize demand in Sub-Saharan Africa without cropland expansion

Maize demand in Sub-Saharan Africa is expected to increase 2.3 times during the next 30 years driven by demographic and dietary changes. Over the past two decades, the area cropped with maize has expanded by 17 million hectares in the region, with limited yield increase. Following this trend could potentially result in further maize cropland expansion and the need for imports to satisfy domestic demand. Here, we use data collected from 14,773 smallholder fields in the region to identify agronomic practices that can improve farm yield gains. We find that agronomic practices related to cultivar selection, and nutrient, pest, and crop management can double on-farm yields and provide an additional 82 million tons of maize within current cropped area. Research and development investments should be oriented towards agricultural practices with proven capacity to raise maize yields in the region.

Assessment of Fertility Status of Soils under Different Cropping Patterns in Rainfed Semi-Arid Eastern Plain of Rajasthan, India

Soil fertility assessment is essential for effective land management practices. Therefore, this study was conducted to assess the physico -chemical characteristics of soil under different cropping systems (Pearl millet (S1), Sorghum (S2), Ground nut (S3), Mung-bean (S4), Maize (S5), Pasture (S6) and to test for significant differences in the nutrient in order to provide basis for recommending site specific land management practices in the study area. Soil samples under the aforementioned cropping systems were collected from 3 sites (Malpura Todaraisingh Piplu) in Tonk District. Collected soil samples were examined for various physico-chemical parameters which includes BD, PD, WHC, Porosity, pH, EC, OC, also macro nutrient like N, P, K, Ca, Mg, and S by using standard analytical methods. Results showed that lowest pH (6.3 to 6.7) observes in sorghum cropping land while highest PH observed in pearl millet (7.4 to 6.7), while Highest organic matter observed in Mung bean cropping system (1.52 and 1.10%) after that ground nut (1.29 and 0.88%). Highest values (568 Kg/ha and 480 Kg/ha) of Nitrogen was obtained from mung bean soil, while lowest in pearl millet and maize (260 Kg/ha and 329 Kg/ha) crop land.

Phosphorus fractions affect fungal compositions and functions under land use conversions in saline-alkali soil in northeastern China

BackgroundLittle is known about the link between the fungal community and phosphorus fractions when land use is converted from cropland to grassland in saline-alkali soil in northeastern China. Therefore, in this study, the diversity, composition, and function of fungi, as well as phosphorus fractions including Olsen-P and inorganic phosphorus (Pi), were investigated under land use conversions from maize cropland (MC) to alfalfa (Medicago sativa L.) (AG), Leymus chinensis (LG), and natural restored grasslands (RG).ResultsThe results showed that the Pi fractions of Ca8-P, Fe-P, Ca2-P, and Ca10-P were closely related with Olsen-P. Significantly increased Olsen-P content was found in 0–10 cm soil layer in the AG treatment, relative to LG and RG treatments (P < 0.05). The occluded P content in 0–10 cm and the Al-P content in 10–20 cm in the RG treatment were the highest. The RG treatment increased the Shannon index of fungi, as well as the abundances of phyla Mortierellomycota and phyla Glomeromycota. Higher abundance of genus Mortierella and lower abundance of genus Cladosporium were observed at RG treatment. Moreover, the RG treatment greatly reduced the abundance of plant pathogens and enhanced the abundances of mycorrhizal and ectomycorrhizal. The Olsen-P was positively correlated with the abundance of plant pathogen (P < 0.01), and the Olsen-P, Ca2-P, and Fe–P were negatively correlated with both the abundances of mycorrhizal and ectomycorrhizal (P < 0.05).ConclusionLand use conversion from maize cropland to natural restored grassland could reduce plant pathogens and enhance useful fungi by decreasing the availability of phosphorus.Graphical

Drought effects on evapotranspiration and energy exchange over a rain-fed maize cropland in the Chinese Loess Plateau

Characterizing dynamics of evapotranspiration (ET) and energy exchange is of particular importance for the water resource use and management in the water-limited agricultural areas under global climate change. Based on eddy covariance method, this study analyzed the seasonal and interannual variations of ET and energy fluxes over a rain-fed spring maize cropland in the Chinese Loess Plateau during 2 years which presented contrasting precipitation (P) totals and patterns in 2019 (drought year) and 2020 (normal year). Results showed obvious intra- and inter-annual variabilities of ET and energy fluxes related with crop phenology and water availability. Annual ET were 339.3 and 386.5 mm yr–1 in 2019 and 2020, respectively, with separate annual ET/P ratios of 0.84 and 0.70. Annual ET was dominated by ET during growing season (May−September), with close proportions of 75.8% and 72.9% for the two years. The partitioning of net radiation (Rn) was dominated by latent heat flux (LE) during growing season, but was primarily driven by sensible heat flux (H) during non-growing season. Annual ratios of LE/Rn and H/Rn were comparable in the drought year (0.46 −0.47), while LE/Rn (0.52) dominated over H/Rn (0.46) in the normal year. Similar seasonal courses were observed in surface conductance (gs), decoupling factor (Ω), and Priestley-Taylor coefficient (α), consistently peaking during the rapid growth period of maize (July−August). The low annual means of Ω (0.20 −0.25) and α (0.44 −0.45) implied the strongly aerodynamical coupling and biophysical control on ET for the study area. Moreover, daily α in this study increased significantly with increasing gs until a threshold around 10 mm s−1. Structural equation modelling revealed that the environmental constraints on ET were mainly affected by Rn, soil water content and vapor pressure deficit (VPD) in the drought year, and prone to be dominated by Rn, leaf area index (LAI) and VPD in the normal year. These results indicated that drought may increase the sensitivity of maize ET to water supply condition, and decrease the sensitivity to changes in available energy, aerodynamic condition and vegetation coverage. This study will provide insights into the effects of drought on energy partitioning and the environmental and biophysical controls on ET in the region.

Soil phosphorus improved the soil organic carbon of converted forestland from cropland within southern Qinling-Daba Mountains, China

Previous studies showed that the program of converted forestland from cropland (CFC), initiated by the Chinese government in 1999, has been a significant contributor to China’s efforts towards carbon neutrality. Here, the 20-year CFCs of two aspects (sunny and shady) and three positions (upper, middle, and lower) hillslopes, adjacent maize ( Zea mays L.) cropland, and natural secondary Castanea mollissima forest (CCF) within southern Qinling-Daba Mountains region (Qinba) had been selected as the targets. The soil bulk density (SBD), soil organic carbon concentration (SOCC), total nitrogen (TN), and total phosphorus (TP) had been determined. The results showed that SBD increased with depth, and other parameters decreased, which varied largely with the aspect and position. The SOC stocks (SOCS) of CCF, cropland, and CFC were 152.81 ± 5.17, 168.19 ± 11.87, and 183.92 ± 35.69 Mg C hm−2, respectively. The SOCCs of CCF, cropland, and CFC were 17.71 ± 4.38, 20.23 ± 5.28, and 21.89 ± 7.33 g kg−1, respectively. The CFC increased the correlations between SOC and TP, and decreased the correlations between SOC and TN. The CFC shifted the relationships of lg SOC versus lg N:P and lg SOC versus lg TP from decreasing returns of cropland to isometric. Overall, the CFCs enhanced SOC, especially in the middle shady hillslopes within the southern middle-mountain of Qinba. In contrast, SOC levels decreased in the sunny upper hillslopes. We presented the evidence that hillslopes aspect and position had significant effects on SOC, which was regulated by soil phosphorus.

The water-energy-food-ecosystem nexus in the Danube River Basin: Exploring scenarios and implications of maize irrigation

The Water-Energy-Food-Ecosystem (WEFE) nexus concept postulates that water, energy production, agriculture and ecosystems are closely interlinked. In transboundary river basins, different sectors and countries compete for shared water resources. In the Danube River Basin (DRB), possible expansion of agricultural irrigation is expected to intensify water competition in the WEFE nexus, however, trade-offs have not yet been quantified. Here, we quantified trade-offs between agriculture, hydropower and (aquatic) ecosystems in the DRB resulting from maize irrigation when irrigation water was withdrawn from rivers. Using the process-based hydro-agroecological model PROMET, we simulated three maize scenarios for the period 2011–2020: (i) rainfed; (ii) irrigated near rivers without considering environmental flow requirements (EFRs); (iii) irrigated near rivers with water abstractions complying with EFRs. Maize yield and water use efficiency (WUE) increased by 101–125 % and 29–34 % under irrigation compared to rainfed cultivation. Irrigation water withdrawals from rivers resulted in moderate to severe discharge reductions and, without consideration of EFRs, to substantial EFR infringements. Annual hydropower production decreased by 1.0–1.9 % due to discharge reductions. However, the financial turnover increase in agriculture (5.8–7.2 billion €/a) was two orders of magnitude larger than the financial turnover decrease in hydropower (23.9–47.8 million €/a), making water more profitable in agriculture. Irrigation WUE was highest for EFR-compliant irrigation, indicating that maintaining EFRs is economically beneficial and that improving WUE is key to attenuating nexus water competition. Current maize production could be met on the most productive 35–41 % of current maize cropland under irrigation, allowing 59–65 % to be returned to nature without loss of production. Maize priority areas were on fertile lowlands near major rivers, while biodiversity priority areas were on marginal cropland of highest biodiversity intactness. Our quantitative trade-off analysis can help identifying science-based pathways for sustainable WEFE nexus management in the DRB, also in light of climate change.

Contribution biochar residue to the role of compost and urea in improving crop yields, soil chemistry properties, and the diversity of soil surface insects in the maize (Zea mays L.) crop land

The objectives of experiments were to study the contribution of biochar residue, compost and urea fertilization on crop yields, soil chemical properties, and the diversity of soil surface insects in the maize crop land. The research was conducted at the University Farm Ie Seum Research Station, Aceh Besar District, Aceh Province, Indonesia. There were three treatments: biochar residue (no biochar residue and biochar residue 30-ton ha−1 or 15 kg plot−1), compost (no compost and compost 30-ton ha−1 or 15 kg plot−1), and urea fertilizer (no urea and urea 200 kg ha−1 or 100 g plot−1). The plant yield parameters were corn weight with cornhusk and no cornhusk and corn production with cornhusk and no cornhusk. The soil chemical properties included pH (H2O), total N, available P, organic C, and cation exchange capacity. Soil surface insects were identified using experimental methods, and insects were collected using a pit fall trap (trap barber). The results showed that the combination of biochar residue and compost resulted in the highest corn yields. The combination of biochar residue, compost, and urea fertilizer increased available P, but not pH (H2O), total N, organic C, and cation exchange capacity. Surface insects on the maize plant land consisting of nine genera were classified into five orders: Blatoidea, Coleoptera, Hemiptera, Hymenoptera, and Orthoptera. Genus Aphaenogaster and Camponotus dominated soil surface insects.

Enhancing Leaf Area Index Estimation for Maize with Tower-Based Multi-Angular Spectral Observations.

The leaf area index (LAI) played a crucial role in ecological, hydrological, and climate models. The normalized difference vegetation index (NDVI) has been a widely used tool for LAI estimation. However, the NDVI quickly saturates in dense vegetation and is susceptible to soil background interference in sparse vegetation. We proposed a multi-angular NDVI (MAVI) to enhance LAI estimation using tower-based multi-angular observations, aiming to minimize the interference of soil background and saturation effects. Our methodology involved collecting continuous tower-based multi-angular reflectance and the LAI over a three-year period in maize cropland. Then we proposed the MAVI based on an analysis of how canopy reflectance varies with solar zenith angle (SZA). Finally, we quantitatively evaluated the MAVI's performance in LAI retrieval by comparing it to eight other vegetation indices (VIs). Statistical tests revealed that the MAVI exhibited an improved curvilinear relationship with the LAI when the NDVI is corrected using multi-angular observations (R2 = 0.945, RMSE = 0.345, rRMSE = 0.147). Furthermore, the MAVI-based model effectively mitigated soil background effects in sparse vegetation (R2 = 0.934, RMSE = 0.155, rRMSE = 0.157). Our findings demonstrated the utility of tower-based multi-angular spectral observations in LAI retrieval, having the potential to provide continuous data for validating space-borne LAI products. This research significantly expanded the potential applications of multi-angular observations.

Mapping annual 10-m maize cropland changes in China during 2017–2021

China contributed nearly one-fifth of the world maize production over the past few years. Mapping the distributions of maize cropland in China is crucial to ensure global food security. Nonetheless, 10 m maize cropland maps in China are still unavailable, restricting the promotion of sustainable agriculture. In this paper, we collect numerous samples to produce annual 10-m maize cropland maps in China from 2017 to 2021 with a machine learning based classification framework. To overcome the temporal variations of plants, the proposed framework takes Sentinel-2 sequence images as input and utilizes deep neural networks and random forest as classifiers to map maize in a zone-specific way. The generated maps have an overall accuracy (OA) spanning from 0.87 to 0.95 and the maize-cultivated areas estimated by the maps are highly consistent with the records in statistical yearbooks (R2 varying from 0.83 to 0.95). To the best of our knowledge, this is the first annual 10-m maize maps across China, which largely facilitates the sustainable agriculture development in China dominated by smallholder farmlands.

Increased extreme humid heat hazard faced by agricultural workers

Increases in population exposure to humid heat extremes in agriculturally-dependent areas of the world highlights the importance of understanding how the location and timing of humid heat extremes intersects with labor-intensive agricultural activities. Agricultural workers are acutely vulnerable to heat-related health and productivity impacts as a result of the outdoor and physical nature of their work and by compounding socio-economic factors. Here, we identify the regions, crops, and seasons when agricultural workers experience the highest hazard from extreme humid heat. Using daily maximum wet-bulb temperature data, and region-specific agricultural calendars and cropland area for 12 crops, we quantify the number of extreme humid heat days during the planting and harvesting seasons for each crop between 1979–2019. We find that rice, an extremely labor-intensive crop, and maize croplands experienced the greatest exposure to dangerous humid heat (integrating cropland area exposed to >27 °C wet-bulb temperatures), with 2001–2019 mean rice and maize cropland exposure increasing 1.8 and 1.9 times the 1979–2000 mean exposure, respectively. Crops in socio-economically vulnerable regions, including Southeast Asia, equatorial South America, the Indo-Gangetic Basin, coastal Mexico, and the northern coast of the Gulf of Guinea, experience the most frequent exposure to these extremes, in certain areas exceeding 60 extreme humid heat days per year when crops are being cultivated. They also experience higher trends relative to other world regions, with certain areas exceeding a 15 day per decade increase in extreme humid heat days. Our crop and location-specific analysis of extreme humid heat hazards during labor-intensive agricultural seasons can inform the design of policies and efforts to reduce the adverse health and productivity impacts on this vulnerable population that is crucial to the global food system.

Contrasting patterns of land-cover change between early and late successional forests in a tropical montane cloud forest area managed under the milpa system

Land cover dynamics (LCD) in areas affected by different management types are hard to predict. Such is the case of tropical montane cloud forest (TMCF) areas in Mexico. The millenary itinerant milpa involves local cyclical deforestation and natural reforestation dynamics with long-lasting fallow periods. Conversely, induced grasslands for cattle, non-traditional maize cultivation with short fallow periods, and sedentary agriculture involve persistent deforestation rates. Also, human density changes could alter LCD. Ignoring such factors may lead to equivocal conclusions. We analyzed LCD using remote sensing analyses with LANDSAT images during two periods (1993–2005, 2005–2014) in a TMCF area in Sierra Norte, Oaxaca, affected by milpas, sedentary agriculture, and grasslands. We divided the study area into six indigenous sub-regions with different human population growth rates (HPG). We identified five land-cover types: mature forests (MF) dominated by TMCF species, early successional pine forests (PF), shrubby fallows (SH), croplands (milpas and more sedentary maize croplands together), and grasslands. We show that: (a) The 1993–2005 period was more variable than the 2005–2014 period, 27 % vs. 21 % of the total area displayed transitions into another class. (b) MF area dominated (277,128 ha), increased from 1993 (64.0 %) to 2014 (67.6 %), and was not statistically associated with HPG. (c) PFs decreased from 27 % in 1993 to 15 % in 2014, and their rate of change was negatively associated with HPG. (d) The total forest area (MF + PF) decreased by 9.3 %. (f) Grasslands, SHs, and croplands fluctuated between periods and sub-regions, but none exceeded 7 % of the total area. (g) Grasslands showed the greatest increase rate, mainly at the expense of forestlands. (h) The grassland and SH increments were positively associated with HPG. The heterogeneity in LCD among periods and sub-regions reveals the relevance of considering temporal and spatial scales within the same region. The persistence of MF and the PF decrease can be ascribed to the disruption of the successional dynamics related to the decline of the traditional milpa enforced by federal and state policies at the beginning of the 21st century, the sedentarization of agriculture, local conservation areas, increases of induced pastures, and human emigration. Although Sierra Norte is an essential reservoir of TMCFs, the decline of the total forest area, particularly secondary forests, is of concern given their roles in resilience and as carbon, culture, and biodiversity reservoirs. Humid mid-elevation areas of Sierra Norte are one of the few examples of mature forest persistence and secondary forest decline.

Impact of straw return combined with different fertilizations on soil organic carbon stock in upland wheat and maize croplands in China: A meta-analysis

Straw return is a vital soil amendment practice in Chinese upland soils, aiming to improve the soil organic carbon (SOC) stock. However, its impact on SOC stock depends on various fertilizer practices, leading to inconclusive results. To address this, we conducted a meta-analysis of 121 peer-reviewed publications to evaluate the effect of straw return combined with different fertilization practices including balanced NPK (BFS), unbalanced NP, NK, PK, or N fertilization (UFS), and no fertilization (NFS) in the wheat-maize cropping system. The results showed that straw return with BFS led to the highest increase in SOC stock (13.3%), followed by UFS (11.4%) and NFS (6.12%), compared to straw removal under the same fertilization conditions. BFS also significantly increased the soil C (15.9%), N (9.62%), and P (6.21%) contents, as well as the C:N (2.48%) and C:P (11.4%) ratios. In contrast, UFS resulted in higher C:N ratios (6.47%) and soil acidification, affecting the SOC stock during straw return. A structural equation model revealed that the presence of BFS positively influenced the association between SOC stock and soil stoichiometry (P ​< ​0.05) compared to UFS, which was influenced by climate and initial soil properties. Additionally, the impact of BFS on SOC stock was more significant in the double cropping system (14.5%) than in the single wheat (8.32%) and single maize (10.8%) cropping systems. A lower initial SOC (<6 ​g ​kg−1) also showed a greater response to BFS compared to a higher initial SOC (>12 ​g ​kg−1). Based on C sequestration efficiency, implementing straw return with BFS could lead to a higher rate of increase in SOC sequestration.

The effect of afforestation type on soil nitrogen dynamics in the riparian zone of the upper Yangtze River of China

Afforestation is increasingly important in nutrient cycling in riparian ecotones given that ecosystems in riparian zones are susceptible to anthropogenic activities induced by land use change. However, how land use change (e.g., afforestation) with different planting types influences nitrogen (N) dynamics in riparian zones remains unclear. Here, we examined soil N dynamics following afforestation with three types of plantations of pure willow (Salix babylonica), pure mulberry (Morus alba), and the mixed two species paired with adjacent maize croplands in the upper Yangtze River of China. Our results showed afforestation with the two pure species significantly reduced soil total N (TN) concentration. Soil NO3−-N concentration was significantly reduced by the willow and mixed-species afforestation, but soil NH4+-N concentration was significantly higher in the willow and mixed woodlands compared to the paired croplands. Soil N concentrations were tightly associated with the potential N transformation rates, which showed a roughly decreasing trend in N mineralization following afforestation. Soil properties, microbial biomass, and extracellular enzymes jointly explained a large proportion of the total variation in soil N concentrations, with soil enzymes largely contributing to N variation in the topsoil and soil properties primarily contributing to N variation in the subsoil. Overall, our results demonstrate that afforestation with different planting types had contrasting effects on soil N content in the riparian zone. These findings provide new insights into the management of afforestation types to retain soil N by mediating soil properties and microbial activities in the riparian zones under future land use change.

Satellite mapping of maize cropland in one-season planting areas of China

sAs the major maize-cultivated areas, the one-season cropland of China is increasingly threatened by rapid urbanization and soybean rejuvenation. Quantifying the area changes of maize cropland is crucial for both food and energy security. Nonetheless, due to the lack of survey data related to planting types, long-term and fine-grained maize cropland maps in China dominated by small-scale farmlands are still unavailable. In this paper, we collect 75,657 samples based on field surveys and propose a deep learning-based method according to the phenology information of maize. With the generalization capability, the proposed method produces maize cropland maps with a resolution of 30 m from 2013 to 2021 in the one-season planting areas of China. The maize-cultivated areas derived from the maps are highly consistent with the data recorded by statistical yearbooks (R2 = 0.85 on average), which indicates that the produced maps are reliable to facilitate the research on food and energy security.

A dataset of water, heat, and carbon fluxes over the winter wheat-summer maize croplands in Luancheng during 2013–2017

The winter wheat-summer maize double-cropping agro-ecosystem holds the largest planting area in the North China Plain. The carbon and water flux observation data of the croplands are crucial to understanding regional carbon and water cycles and their regulation mechanism. Located in the piedmont Plain of Taihang Mountain, Luancheng Agro-ecosystem Experimental Station (Luancheng Station) is the typical groundwater irrigation district. We have been using the eddy covariance technology to carry out the observation work of the winter-summer maize croplands since October 2007. This is the dataset of water, heat, and carbon fluxes over the winter wheat-summer maize croplands in Luancheng from October 2013 to September 2017 (covering four complete growing seasons). The dataset includes latent heat flux (or evapotranspiration), sensible heat flux, net ecosystem exchange of CO2, solar radiation, photosynthetically active radiation, air temperature/humidity, precipitation, soil temperature and volumetric soil water content at the half-hourly, daily, crop-season, and crop-year scales. The network observation, quality control and storage process of this dataset strictly abide by ChinaFLUX data management technology system to ensure data reliability. This dataset can provide solid data support for deeply understanding the processes of water, heat and carbon exchange, and improving the management of water resource to relieve the overdraft of groundwater in North China.

Impacts of straw return coupled with tillage practices on soil organic carbon stock in upland wheat and maize croplands in China: A meta-analysis

Despite numerous individual studies evaluating the impact of straw return on soil organic carbon (SOC) stock in upland wheat and maize croplands in China using no-tillage (NTS), rotary tillage (RTS), and deep tillage (DTS) practices, the results have shown inconsistency. In addition, several factors influence the effects of various tillage practices with straw return on SOC stock that are not well understand. In this study, we conducted a meta-analysis of 764 comparisons to determine the combined impact of SOC stock in the topsoil (0–20 cm) and subsoil (20–40 cm) layers, while considering factors such as climate conditions, management practices, and initial soil properties. The results indicate that straw return with different tillage practices significantly improved SOC stock in both soil layers compared to straws removal. The increase in topsoil SOC stock was nearly identical for NTS (7.38 %) and DTS (7.17 %), compared to the RTS. However, the increase in subsoil SOC stock was significantly higher for DTS (28.8 %). According to subgroup analysis, NTS has been more effective in increasing topsoil SOC stock in regions with medium temperature (10–15 °C) and low precipitation (<400 mm), when used in a single cropping system. However, in regions with higher temperature (>15 °C) and precipitation (>800 mm), DTS is more effective in increasing SOC stock in both topsoil and subsoil layers in a double cropping system. Long-term (11–20 years) DTS resulted in a significant increase in SOC stock in the topsoil (19.2 %) and subsoil (18.5 %), while the increased effects of NTS on SOC stock were only in topsoil layer (17.1 %). The effects of straw return on SOC stock were also found to be associated with soil texture, with NTS having a greater impact on sandy to loam soils, and DTS having a greater impact on clay soils. Furthermore, a significant (P < 0.01) positive linear relationship was observed between annual carbon (C) input and SOC sequestration in the topsoil under NTS, as well as in both soil layers under DTS. These findings have important implications for enhancing SOC in Chinese upland wheat and maize cropland soils through optimizing straw return, given that more than 518 million tons of crop straw are produced annually.

Mapping Maize Cropland and Land Cover in Semi-Arid Region in Northern Nigeria Using Machine Learning and Google Earth Engine

The monitoring of crop quantity and quality is vital for global food security. National food security has recently been at the forefront of local and regional research, and has become a vital priority for most developing countries. Therefore, ensuring reliable classification of cropland and other land cover is crucial for sustainable agricultural development and ensuring national food security. A good understanding of the Nigerian agricultural sector is essential to making better decisions and managing operations more efficiently. Scientists, practitioners, and policymakers must exchange reliable information to develop and support agricultural programs and policies. It is essential to develop and implement novel methods for mapping maize cropland and other land cover types. Thus, Seasonal Crop Inventory (SCI) is a valuable tool for farmers, researchers, and policymakers, as it provides critical information on crop production. It informs decisions related to land management, food security, and agricultural policy. In this study, Sentinel-1 and Sentinel-2 images have been combined to map maize cropland and other land covers in northern Nigeria during the 2016–2019 growing season. We employed a technologically advanced space-based remote sensing technique. As a pioneer study that obtained detailed information on northern Nigeria’s cropland, the research utilized platforms such as Google Earth Engine (GEE), a cloud-computing engine using various classification techniques that include Random Forest (RF), Support Vector Machine (SVM), and Classification Regression Trees (CART) algorithms to produce a pixel-based Seasonal Crop Inventory of the study area. The outcome demonstrated a reliable GEE-based mapping of the region’s cropland with satisfactory classification accuracy. It revealed the overall accuracy values and the Kappa coefficients to be above 97% during the different time nodes under study. It also indicated a 98% and 93% producer and user accuracy for the cropland. The research further revealed that the Random Forest performed the best among the three machine-learning models tested in this study for mapping the maize cropland and other land cover classes. Therefore, the study’s findings and the derived crop mapping would greatly help provide valuable information that helps farmers, policymakers, and other stakeholders make more informed decisions about agricultural production, land use planning, and resource management.

Estimating evapotranspiration and yield of wheat and maize croplands through a remote sensing-based model

Estimating evapotranspiration and yield of wheat and maize croplands through a remote sensing-based model

Soil CO2 emission reduction with no-tillage and medium nitrogen fertilizer applications in semi-humid maize cropland in North China Plain

To better understand the effects of tillage practices and nitrogen (N) fertilizer applications on the microbial-mediated soil CO2 emissions in the maize fields, a four-year (2018–2021) experiment was performed to compare conventional tillage (CT) and no-tillage (NT) in combination with different N rates (high N (HN) and medium N (MN) applications) on the temporal dynamics of CO2 emissions in the soil. The results showed that soil CO2 emissions were decreased by 10.3%24.5% and 18.9%31.8% under NT and MN as compared to CT and HN, respectively. The lowest and highest soil cumulative CO2 emissions occurred in the NTMN treatment (a recommended pattern) and CTHN treatment (an extensively used pattern locally), respectively. Relative to CT and HN, NT and MN increased the grain yields by 1.4%9.0% and 7.0%, respectively. NT decreased bacterial richness and alpha-diversity by 9.8% and 6.2% at the taxonomic level than CT. Meanwhile, NTMN reduced fungal diversity by 0.7%, bacterial diversity by 3.4% and bacterial richness by 0.3% compared to CTHN. NT and MN inhibited metabolic processes associated with carbon (C) cycling and enhanced C fixation pathways in prokaryotes. NTMN treatment reduced substrate availability and energy sources for fungal and bacterial activities by altering soil biochemistry, further decreasing fungal and bacterial diversity, suppressing soil CO2 emissions, and increasing yields, which were in turn enhanced by changes in soil temperature, moisture, and bulk density. This research emphasized the necessity to explore eco-friendly cropland practices in maize cultivation zones, which would contribute to the sustainable development of the agricultural sector with positive effects on reducing soil CO2 emissions and increasing crop yields. However, stability and validity need further investigation on long-term scales.