Crop-specific Soil Research Articles

Responsive change of crop-specific soil bacterial community to cadmium in farmlands surrounding mine area of Southeast China

In arable soils co-influenced by mining and farming, soil bacteria significantly affect metal (Cadmium, Cd) bioavailability and accumulation. To reveal the soil microecology response under this co-influence, three intersection areas (cornfield, vegetable field, and paddy field) were investigated. With a similar nutrient condition, the soils showed varied Cd levels (0.31–7.70 mg/kg), which was negatively related to the distance from mining water flow. Different soils showed varied microbial community structures, which were dominated by Chloroflexi (19.64–24.82%), Actinobacteria (15.49–31.96%), Acidobacteriota (9.46–20.31%), and Proteobacteria (11.88–14.57%) phyla. A strong correlation was observed between functional microbial taxon (e. g. Acidobacteriota), soil physicochemical properties, and Cd contents. The relative abundance of tolerant bacteria including Vicinamibacteraceae, Knoellia, Ardenticatenales, Lysobacter, etc. elevated with the increase of Cd, which contributed to the enrichment of heavy metal resistance genes (HRGs) and integration genes (intlI), thus enhancing the resistance to heavy metal pollution. Cd content rather than crop species was identified as the dominant factor that influenced the bacterial community. Nevertheless, the peculiar agrotype of the paddy field contributed to its higher HRGs and intlI abundance. These results provided fundamental information about the crop-specific physiochemical-bacterial interaction, which was helpful to evaluate agricultural environmental risk around the intersection of farmland and pollution sources.

Nutrient dynamics under unmanaged rubber, cocoa, and oil palm plantations in a sandy soil under humid lowland tropical climatic conditions

Changes in land use are an important issue in many farms that affect soil biological, chemical, and physical properties temporarily under cropping cycles or when the land is permanently allocated to perennial tree crops, e.g., in agroforestry. This study investigated the changes in sandy soil chemistry induced by three perennial tree crops (rubber, cocoa, and oil palm) growing in 30-year-old unmanaged and abandoned plantations and the surrounding grasslands dominated by cogon grass. A disruptive approach was used to collect soil samples from the top 60 cm under all the tree crops and in the grassland soils. A 500-gram sample of each soil originating from under each tree crop and the grassland were carefully packed into pre-labeled paper bags in triplicate (n=3) and sent to the laboratory for analysis of a selected number of primary and secondary macronutrients, micronutrients, and other soil parameters. The results showed N, K, Mg, Cu, Zn, and S were generally deficient in the sandy soil. A tree crop-specific soil organic matter, organic carbon, carbon stock contents, and water holding capacity measured were high under rubber and cocoa only. The variation in pH, electrical conductivity, bulk density, total porosity, and particle composition were generally similar except that the sand composition was lower in the soils under rubber and oil palm.

Data article on soil site suitability analysis using geostatistical and visualization techniques for selected winter crops in Sagar island, India.

We assessed soil site suitability for selected winter crops in the coastal saline agro-ecological environment of Sagar Island, India by integrating land limitation and crop suitability evaluation framework of FAO. Grid based (1 km by 1 km) soil sampling and estimation of important soil quality attributes were measured in the laboratory following standard procedures. Geo-statistical and visualization methods were applied to match the soil suitability for selected crops. The weights of crop specific soil parameters have been assigned through PCA analysis. The inverse distance weighting interpolation and reclassification methods were adopted for generation of spatial layers of those soil attributes. Nearly 61% area (14,618 ha GA) of the Island is under agricultural landuse (AL), mostly dominated (>75% of AL) by lowland rice-fallow mono-cropping. Soils are highly suitable (S1) for growing sunflower while moderately suitable (S2) for growing chilli, mustard and potato crops. The grid-wise georeferenced soil data information generated in this study will help in periodic monitoring of soil quality in spatio-temporal dimensions for devising location specific soil health managements in the Island. The methodology used in estimating soil quality index and crop specific soil suitability analysis in spatial format will help in replicating such studies in other such coastal Islands of Indian Sub-continent.

Statistical modelling of drought-related yield losses using soil moisture-vegetation remote sensing and multiscalar indices in the south-eastern Europe

Meteorological and agricultural information coupled with remote sensing observations has been used to assess the effectiveness of satellite-derived indices in yield estimations. The estimate yield models generated by both the regression (MLR) and Bayesian network (BBN) algorithms and their levels of predictive skill were assessed. The enhanced vegetation index (EVI2), soil water index (SWI), standardized precipitation evaporation index (SPEI) have been considered predictors for three rainfed crops (maize, sunflower and grapevine) grown in 37 districts in the Republic of Moldova (RM). We used the weekly EVI2, which was collected by MODIS instruments aboard the Terra satellite with a 250m × 250m spatial resolution and aggregated for each district during the 2000–2018 period. We also used the weekly SWI, which was collected from the ASCAT instruments with a 12 km x 12 km spatial resolution and aggregated for each district at the topsoil (0–40 cm; SWI-12) and the root-zone layer (0–100 cm; SWI-14) during 2000–2018. The multiscalar SPEI during 1951–2018 farming years proved to be a significant addition to the remote sensing indices and led to the development of a model that improved the yield assessment. The study also summarized (i) the optimal time window of satellite-derived SWIi and EVI2i for yield estimation, and (ii) the capability of remotely sensed indices for representing the spatio–temporal variations of agricultural droughts. We developed statistical soil-vegetation-atmosphere models to explore drought-related yield losses. The skill scores of the sunflower MLR and BBN models were higher than those for the maize and grape models and were able to estimate yields with reasonable accuracy and predictive power. The accurate estimation of maize, sunflower and grapevine yields was observed two months before the harvest (RMSE of ∼1.2 tha-1). Despite the fact that summer crops (maize, sunflower) are able to develop a root system that uses the entire root zone depth, however, the SWI-12 had the stronger correlation with crop yield, then SWI-14. This explains much better the fit between yields of the crops and SWI-12, which represents soil moisture anomaly in the key rooting layer of soil. In any case, all summer crops showed negative correlations with each of the remote sensing soil moisture indices in the early and middle of the growing season, with SWI-12 performing better than SWI-14. Based on the crop-specific soil moisture model, we found that topsoil moisture declines in the most drought-susceptible crop growth stages, which indicates that RM is a good candidate for studying drought persists as main driver of rainfed yield losses in the south-eastern Europe.

Soil microclimates influence annual carbon loss via heterotrophic soil respiration in maize and switchgrass bioenergy cropping systems

Heterotrophic soil respiration (RH) is the primary pathway of carbon (C) loss from litter and soil organic matter, and thus RH partially determines ecosystem C storage. Because RH is sensitive to soil temperature and moisture, aboveground factors that influence soil microclimate, such as plant structure and residue management, may in turn affect belowground C loss via RH, but this relationship has not been quantified. We examined multiyear soil microclimate differences to 1-m depth, measured seasonal trends of RH, and parameterized crop-specific microclimate-RH models to quantify the effect of soil microclimate differences on annual RH in temperate no-till maize and switchgrass bioenergy cropping systems. Summertime soil temperatures were typically warmer in maize compared to switchgrass, likely resulting from lower leaf area index (LAI) in maize. In contrast, winter soil temperatures were usually warmer in switchgrass than maize, due in part to more consistent snow retention within the switchgrass litter stubble. Daily soil temperature ranges were less extreme in the perennial switchgrass system compared to the annual no-till maize system. Soil moisture near the soil surface was usually lower in maize than switchgrass, but the opposite was true below about 50 cm. RH showed strong seasonal trends, with warmer and drier soil conditions generally leading to higher RH in both crops. Modeled scenarios indicated that the differences in crop-specific soil microclimates accounted for 4 to 17% of the annual RH flux, with the dominant soil microclimate effects on RH occurring during the summer. Thus, the soil microclimate serves as a strong coupling between aboveground properties and belowground C loss via RH in temperate agroecosystems. Agricultural management practices such as planting date, plant density, and residue management could be targeted to promote soil microclimates that reduce RH, thereby reducing gaseous belowground C losses and potentially enhancing ecosystem C storage.

Agroclimatic zoning methodology for agricultural production systems in dry Caribbean region of Colombia

The agricultural sector in Colombia and especially, small-scale agriculture subsector in the Colombian Caribbean has been very vulnerable to the negative impacts of climate variability in part due to the poor existence and lack of access to agro-climatic information. The objective was to build a methodology for agroclimatic zoning for crops growing in Colombian dry Caribbean. To achieve this goal, a case study of tomato crop growing in the municipality of Repelon (Atlantico, Colombia) was selected. The methodology was based in the combination of crop-specific soil suitability assessment and the monthly prob ability of a humidity condition occurrence in the soil for tomato production under water deficit. Probability was constructed through water balances generated from the Palmer drought severity index, calculated for each month of crop production cycle during the 1980 to 2011 series. The resulting maps show defined areas called Productive Niches with lower limitations per soil and low probabilities of soil water deficit in the growth of tomato crop in the municipality. The methodology can be used to delineate areas suitable for planting tomato or other crops in climate variability events in the dry Caribbean region of Colombia.

Assessment of soil loss and nutrient depletion due to cassava harvesting: A case study from low input traditional agriculture

Cassava is a major food crop for farmers and especially small holder farmers and cultivated under low input other than the irrigation. It is cultivated as mono crop or intercrop at early stage and cultivating throughout the year. It is harvested carefully because of cyanogenic glucosides and consumed with in day. Easy and un-damageable uprooting of the tuber mainly depends on soil moisture, texture and agronomic practices. The study was focused with the objectives of the assessment of soil loss due to the harvesting of cassava roots tubers under low input agriculture, and to estimate the amount of plant nutrients loss due to crop harvest for cassava. Also the observation was made the correlation between the soil loss and physical characters of the tuber, soil texture and agronomic practices. Average plant specific soil loss due to crop harvesting was 80. 7 g root−1 and crop specific soil loss due to crop harvesting was 7. 64 kg ha−1 harvest−1 loss in Valliagmam area in Jaffna, Sri Lanka. Soil moisture content at harvesting time was a significant factor that explained the variations in the soil lost at cassava harvesting. Soil moisture has linear positive relationship with average plant specific soil loss due to crop harvesting. Soil nutrient loss during cassava harvesting by removal of adhering soil with root tuber was 1. 15 kg of N, 1. 99 kg of P and 2. 91 kg of K ha−1 harvest−1. Application of fertilizer is important since considerable amount nutrient loss was observed due to soil loss due to crop harvest.

New Strategy for Optimizing Water Application under Trickle Irrigation

The determination of water application parameters for creating an optimal soil moisture profile represents a complex nonlinear optimization problem which renders traditional optimization into a cumbersome procedure. For this reason, an alternative methodology is proposed which combines a numerical subsurface flow model and artificial neural networks (ANN) for solving the problem in two, fully separate steps. The first step employs the flow model for calculating a large number of wetting profiles (output), obtained from a systematic variation of both water application and initial soil moisture (input). The resulting matrix of corresponding input/output values is used for training the ANN. The second step, the application of the fully trained ANN, then provides the irrigation parameters which range from a specified initial soil moisture to a desired crop-specific soil moisture profile. In order to avoid substantial disadvantages associated with the common feedforward backpropagation approach, a self-organizing topological feature map is implemented to perform this task. After a comprehensive sensitivity analysis, the new methodology is applied to the outcome of an irrigation experiment. The convincing results recommend the new methodology as a positive contribution towards an improved irrigation efficiency.