Geology ReportsSearch

USGS · 70176301

Biochar effect on maize yield and soil characteristics in five conservation farming sites in Zambia

Abstract

Biochar addition to agricultural soils can improve soil fertility, with the added bonus of climate change mitigation through carbon sequestration. Conservation farming (CF) is precision farming, often combining minimum tillage, crop rotation and residue retention. In the present farmer-led field trials carried out in Zambia, the use of a low dosage biochar combined with CF minimum tillage was tested as a way to increase crop yields. Using CF minimum tillage allows the biochar to be applied to the area where most of the plant roots are present and mirrors the fertilizer application in CF practices. The CF practice used comprised manually hoe-dug planting 10-L sized basins, where 10%–12% of the land was tilled. Pilot trials were performed with maize cob biochar and wood biochar on five soils with variable physical/chemical characteristics. At a dosage as low as 4 tons/ha, both biochars had a strong positive effect on maize yields in the coarse white aeolian sand of Kaoma, West-Zambia, with yields of 444% ± 114% ( p = 0.06) and 352% ± 139% ( p = 0.1) of the fertilized reference plots for maize and wood biochar, respectively. Thus for sandy acidic soils, CF and biochar amendment can be a promising combination for increasing harvest yield. Moderate but non-significant effects on yields were observed for maize and wood biochar in a red sandy clay loam ultisol east of Lusaka, central Zambia (University of Zambia, UNZA, site) with growth of 142% ± 42% ( p > 0.2) and 131% ± 62% ( p > 0.2) of fertilized reference plots, respectively. For three other soils (acidic and neutral clay loams and silty clay with variable cation exchange capacity, CEC), no significant effects on maize yields were observed ( p > 0.2). In laboratory trials, 5% of the two biochars were added to the soil samples in order to study the effect of the biochar on physical and chemical soil characteristics. The large increase in crop yield in Kaoma soil was tentatively explained by a combination of an increased base saturation (from <50% to 60%–100%) and cation exchange capacity (CEC; from 2–3 to 5–9 cmol/kg) and increased plant-available water (from 17% to 21%) as well as water vapor uptake (70 mg/g on maize cob biochar at 50% relative humidity).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gerard Cornelissen, Vegard Martinsen, Victor Shitumbanuma, Vanja Alling, Gijs D. Breedveld, David W. Rutherford, Magnus Sparrevik, Sarah E. Hale, Alfred Obia, Jan Mulder. 2013-04-11. Biochar effect on maize yield and soil characteristics in five conservation farming sites in Zambia. https://doi.org/10.3390/agronomy3020256

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Multispectral red-edge indices accurately estimate nitrogen content in winter cereal cover crops

Winter cover crops reduce erosion and nutrient runoff from agricultural systems. Although cereal cover crops can decrease field nitrate leaching by 50%–95%, the magnitude of this reduction varies within and between fields, making it challenging to monitor the impact of cover crops on nitrate leaching at large spatial extents. Satellite remote sensing using red-edge bands has been shown to effectively estimate crop nitrogen (N) content (kg ha −1 ) in later growth-stage crops with a closed canopy. In this study, we evaluated 15 spectral indices derived from Sentinel-2 imagery to estimate N concentration (%) and content (kg ha −1 ) of cereal cover crops, using 1627 destructive samples collected from 2018 to 2023 in Maryland. Observed N content ranged from 0.1 to 214.7 kg ha −1 , while N concentration ranged from 0.6% to 5.5%. The 15 indices considered were poor predictors of N concentration (adj. R 2 = 0.089, root mean squared error [RMSE] = 0.802%), but were more successful at measuring N content (biomass × N concentration). Delta red-edge (ΔRE) was the best predictor of N content (adj. R 2 = 0.748, RMSE = 13.10 kg ha −1 from cross-validation with 80% train and 20% test splits iterated 100 times) using samples with imagery collected within ±4 days of destructive sampling ( n = 1110). Our findings indicate that longer red-edge wavelengths (783 and 740 nm) are more suited for estimating N content in cereal cover crops compared to shorter red-edge wavelengths, which have been shown to be more sensitive to biomass. Leave-one-year-out cross-validation demonstrated that the relationship between ΔRE and N content was robust across all four cover crop sampling years included in the study (adj. R 2 = 0.700–0.769, RMSE = 10.70–15.40 kg ha −1 ). Regression model performance improved with the addition of multiple predictors, including biomass (estimated from Normalized Difference Vegetation Index), weather variables (adj. R 2 = 0.765, RMSE = 12.37 kg ha −1 ), management variables (species, season, adj. R 2 = 0.772, and RMSE = 12.13 kg ha −1 ), and biophysical variables (height, fractional ground cover, adj. R 2 = 0.818, and RMSE = 10.29 kg ha −1 ). These findings demonstrate the feasibility of quantifying N content in cereal cover crops using a red-edge-based spectral index across large geographic extents and indicate the inclusion of additional predictors, such as weather and management data, improves model accuracy. This work has implications for quantifying reductions in N leaching associated with cover crops, aiding in policymaking and evaluation of conservation programs that impact water bodies such as Chesapeake Bay.

Maryland

Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA

Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data.

Maryland

Remote sensing evaluation of winter cover crop springtime performance and the impact of delayed termination

In 2019, the Maryland Department of Agriculture's Winter Cover Crop Program introduced a delayed termination incentive (after May 1) to promote springtime biomass accumulation. We used satellite imagery calibrated with springtime in situ measurements collected from 2006–2021 ( n = 722) to derive biomass estimates for Maryland fields planted to cereal cover crop species (286,200 ha total over two seasons). Cover crop C content remained steady throughout the cover crop growing season (42.6% of biomass), whereas N concentration had an inverse relationship with biomass and ranged from 1.7 to 2.9%. Throughout Maryland, delayed termination fields ( n = 19,120; average termination of May 18) were, on average, estimated to accumulate an additional 789 kg of biomass, 15 kg of N, and 336 kg of C per hectare when compared to fields associated with standard termination dates ( n = 28,811; average termination of April 16). Over two cover crop seasons (2019–2021), the delayed termination incentive yielded an extra 75,660,000 kg biomass, 1,526,000 kg N, and 32,230,000 kg C across 96,040 hectares. Regularly terminated field incentives cost an average of US$0.10 per kg of biomass and $4.09 per kg of N, with variability associated with agronomic management (species, planting method). Delayed termination fields cost of $0.08 per kg of biomass and $3.51 per kg of N. Late-planted cover crops that were terminated early had minimal environmental benefit, and wheat, which comprised 68% of cover crop area, performed poorly compared with other cereal species. Our findings demonstrate that substantial additional springtime biomass, C, and N accumulation were achieved through the delayed termination incentive.

Maryland