Abdu Abass, Laekemariam Fanuel, Gidago Gifole, Kebede Abiyot, Getaneh Lakew
Department of Plant Sciences, Wolaita Sodo University, Wolaita Sodo, Ethiopia.
Heliyon. 2023 Mar 2;9(3):e14013. doi: 10.1016/j.heliyon.2023.e14013. eCollection 2023 Mar.
Agricultural productivity is significantly impacted by soil properties, which vary spatially from a small to a larger area. This variation may be caused by a combination of intrinsic and extrinsic factors, including human activities like soil management practices. The aim of the current study was to analyze soil spatial variability, create a Digital Soil Map (DSM), and test map information with crop in Southern Ethiopia. A total of 18 geo-referenced surface soil samples at depth of 20 cm were collected. Selected soil Physico-chemical properties such as soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), sulfur (S), exchangeable bases [calcium (Ca), magnesium (Mg), and potassium (K)], soil micronutrients [boron (B), copper (Cu), iron (Fe), manganese (Mn) and zinc (Zn)] and cation exchange capacity (CEC) were analyzed. The results revealed clay texture with a mean pH value of 4.6 (strong acidity). About 50% of essential nutrients [N, P, S, Ca, Mg, B & Fe] were deficient. The geostatistical analysis has shown that the best-fitted models were exponential for (OC, TN, available P, S, Mg, CEC, B, Fe, and Zn), spherical for (pH, Ca, Cu and Mn), and Gaussian for (C:N, K, K:Mg, and PBS). The range of all soil properties varied from 50 m to 84 m which was above the actual distance between soil samples (i.e., 46 m). The result showed that the spatial dependence values for soil properties of [OC, TN, CEC, PBS, ESP, and Cu]; [pH, C: N ratio, available P, S, Ca, Mg, K, Na, K: Mg ratio and Zn] and [B, Fe, M n] were strong (<25%]; weak (>75%) and moderate (25%-75%), respectively. Model performance using indicators such as prediction mean error (PME), root mean square standardized error (RMSSE), mean standard error (MSE), and root-mean-square error (RMSE) also confirmed the acceptable prediction. The DSM demonstrated the limitation of N, P, S, and B nutrients for intervention. The DSM information was tested under field conditions using haricot bean (Phaseolus vulgaris) with lime and organic fertilizers as treatments. The experiment consists of lime rates (0, 3, 6 t/ha), rhizobium inoculation (inoculated and non-inoculated), and fertilizer types (0, 150 kg ha NPSB, 5 t/ha vermi compost, 10t/ha farmyard manure (FYM)) in Randomized Complete Block Design with three replications. The result exhibited interaction effects of lime, inoculation, and fertilizer types significantly influenced (p < 0.05) biomass and grain yield of haricot beans. Rhizobium inoculation x 6t/ha lime x 150 kg ha NPSB recorded the maximum grain yield (3186.1 kg/ha) which was 26.3 fold over the non-treated soil (117 kg ha). In conclusion, the DSM classified the area into distinct management zones which were tested with a crop trial. The results of the trial confirm the importance of site-specific nutrients/amendment application for sustainable soil management.
土壤性质对农业生产力有显著影响,其在空间上从小面积到大面积都存在差异。这种差异可能由内在和外在因素共同导致,包括土壤管理措施等人类活动。本研究的目的是分析埃塞俄比亚南部土壤的空间变异性,创建数字土壤图(DSM),并在作物上测试地图信息。共采集了18个深度为20厘米的地理参考表层土壤样本。分析了选定的土壤物理化学性质,如土壤质地、pH值、有机碳(OC)、总氮(TN)、有效磷(av.P)、硫(S)、交换性碱[钙(Ca)、镁(Mg)和钾(K)]、土壤微量元素[硼(B)、铜(Cu)、铁(Fe)、锰(Mn)和锌(Zn)]以及阳离子交换容量(CEC)。结果显示土壤质地为黏土,平均pH值为4.6(强酸性)。约50%的必需养分[N、P、S、Ca、Mg、B和Fe]缺乏。地统计分析表明,最佳拟合模型对于(OC、TN、有效P、S、Mg、CEC、B、Fe和Zn)为指数模型,对于(pH、Ca、Cu和Mn)为球形模型,对于(C:N、K、K:Mg和PBS)为高斯模型。所有土壤性质的变程在50米至84米之间,高于土壤样本之间的实际距离(即46米)。结果表明,[OC、TN、CEC、PBS、ESP和Cu]、[pH、C:N比、有效P、S、Ca、Mg、K、Na、K:Mg比和Zn]以及[B、Fe、Mn]的土壤性质空间依赖性值分别为强(<25%)、弱(>75%)和中等(25%-75%)。使用预测平均误差(PME)、均方根标准化误差(RMSSE)、平均标准误差(MSE)和均方根误差(RMSE)等指标的模型性能也证实了预测是可接受的。DSM显示了N、P、S和B养分干预的局限性。在田间条件下,以石灰和有机肥料为处理,使用菜豆(Phaseolus vulgaris)对DSM信息进行了测试。试验包括石灰施用量(0、3、6吨/公顷)、根瘤菌接种(接种和未接种)以及肥料类型(0、150千克氮磷硫硼/公顷、5吨/公顷蚯蚓堆肥、10吨/公顷农家肥(FYM)),采用随机完全区组设计,重复三次。结果表明,石灰、接种和肥料类型的交互作用对菜豆的生物量和籽粒产量有显著影响(p<0.05)。根瘤菌接种×6吨/公顷石灰×150千克氮磷硫硼/公顷的籽粒产量最高(3186.1千克/公顷),是未处理土壤(117千克/公顷)的26.3倍。总之,DSM将该区域划分为不同的管理区,并通过作物试验进行了测试。试验结果证实了针对特定地点施用养分/改良剂对可持续土壤管理的重要性。